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1
Bai, Yang, Chengqian Sun, Li Wang, Yang Wu, Jiaman Qin, and Xi Zhang. "The Characteristics of Net Anthropogenic Nitrogen and Phosphorus Inputs (NANI/NAPI) and TN/TP Export Fluxes in the Guangdong Section of the Pearl River (Zhujiang) Basin." Sustainability 14, no. 23 (December 3, 2022): 16166. http://dx.doi.org/10.3390/su142316166.
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Human activities have greatly influenced the inputs and cycling pathways of nitrogen (N) and phosphorus (P), causing dramatic environmental problems in the Pearl River Basin. In this study, the characteristics of net anthropogenic nitrogen and phosphorus inputs (NANI/NAPI) were analyzed in the Guangdong section of the Pearl River Basin from 2016 to 2020. NANI showed a very slight decrease trend from (1.51 ± 0.09) × 104 to (1.36 ± 0.08) × 104 kg·N·km−2·yr−1, while the average intensity of NAPI was 3.8 × 103 kg·P·km−2·yr−1. Both NANI and NAPI intensities were at high levels, resulting in the serious deterioration of water quality in the Pearl River Basin. Fertilizer input was the most important component for the intensities of NANI and NAPI, accounting for 38–42% and 53–56%. However, in the Pearl River Delta, the major components of NANI and NAPI were the human and animal consumption (food/feed) inputs and non-food net phosphorus input. The input of NANI and NAPI should be controlled for different areas, based on the differing driving forces, to alleviate the deterioration of water quality. This study of NANI and NAPI in the Pearl River Basin is one of the important prerequisites for clarifying the input and water quality, providing support for further effective control of nitrogen and phosphorus pollution in the Pearl River.
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Shah, Muhammad Izhar, Taher Abunama, Muhammad Faisal Javed, Faizal Bux, Ali Aldrees, Muhammad Atiq Ur Rehman Tariq, and Amir Mosavi. "Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization." Sustainability 13, no. 8 (April 20, 2021): 4576. http://dx.doi.org/10.3390/su13084576.
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Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the developed models was estimated by statistical assessment indicators. Moreover, the 10-fold cross-validation method was carried out to address the modeling overfitting issue. The results of the input optimization indicate that Ca2+, Na+, and Cl− are the most relevant inputs to be used for EC. Meanwhile, Mg2+, HCO3−, and SO42− were selected to model TDS levels. The optimum ANFIS models for the EC and TDS data showed R values of 0.91 and 0.92, and the root mean squared error (RMSE) results of 30.6 µS/cm and 16.7 ppm, respectively. The optimum ANFIS structure comprises a hybrid training algorithm with 27 fuzzy rules of triangular fuzzy membership functions for EC and a Gaussian curve for TDS modeling, respectively. Evidently, the outcome of the present study reveals that the ANFIS modeling, aided with data pre-processing and input optimization, is a suitable technique for simulating the quality of surface water. It could be an effective approach in minimizing modeling complexity and elaborating proper management and mitigation measures.
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Pratap, Preethi L., Sarah Redman, Michael C. Fagen, and Samuel Dorevitch. "Improving water quality communications at beaches: input from stakeholders." Journal of Water and Health 11, no. 4 (August 19, 2013): 647–58. http://dx.doi.org/10.2166/wh.2013.077.
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Objectives: Water quality communication practices vary widely and stakeholder input has not played a role in defining acceptable levels of risk. Although the 2012 Recreational Water Quality Criteria (RWQC) emphasize the importance of promptly notifying the public about hazardous conditions, little is known about the public's understanding of notifications, or about levels of risk deemed acceptable. We sought to address these gaps. Methods: A mixed methods approach was used. Focus groups (FGs) provided qualitative data regarding the understanding of surface water quality, awareness, and use, of currently available water quality information, and acceptability of risk. Intercept interviews (INTs) at recreation sites provided quantitative data. Results: INTs of 374 people and 15 FG sessions were conducted. Participants had limited awareness about water quality information posted at beaches, even during swim bans and advisories. Participants indicated that communication content should be current, from a trusted source, and describe health consequences. Communicating via mobile electronics should be useful for segments of the population. Risk acceptability is lower with greater outcome severity, or if children are impacted. Conclusions: Current water quality communications approaches must be enhanced to make notification programs more effective. Further work should build on this initial effort to evaluate risk acceptability among US beachgoers.
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Kumar, Surender. "Analysing industrial water demand in India: An input distance function approach." Water Policy 8, no. 1 (February 1, 2006): 15–29. http://dx.doi.org/10.2166/wp.2006.0002.
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This study investigates the water demand of Indian manufacturing plants. It adopts an input distance function approach and approximates it by a translog form. Duality between cost function and input distance function is exploited to retrieve information concerning substitutability and the shadow price of water. The model is estimated using a linear programming approach on a sample of 92 firms over three years. The results show that the average shadow price of water is 7.21 Rupees per kilolitre (Rupees/kl) and the price elasticity of derived demand for water is high, −1.11 on average, a value similar to that found by other researchers working in developing countries (for example, China and Brazil). This indicates that water charges may be an effective instrument for water conservation.
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Catenacci, Arianna, Matteo Grana, Francesca Malpei, and Elena Ficara. "Optimizing ADM1 Calibration and Input Characterization for Effective Co-Digestion Modelling." Water 13, no. 21 (November 4, 2021): 3100. http://dx.doi.org/10.3390/w13213100.
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Anaerobic co-digestion in wastewater treatment plants is looking increasingly like a straightforward solution to many issues arising from the operation of mono-digestion. Process modelling is relevant to predict plant behavior and its sensitivity to operational parameters, and to assess the feasibility of simultaneously feeding a digester with different organic wastes. Still, much work has to be completed to turn anaerobic digestion modelling into a reliable and practical tool. Indeed, the complex biochemical processes described in the ADM1 model require the identification of several parameters and many analytical determinations for substrate characterization. A combined protocol including batch Biochemical Methane Potential tests and analytical determinations is proposed and applied for substrate influent characterization to simulate a pilot-scale anaerobic digester where co-digestion of waste sludge and expired yogurt was operated. An iterative procedure was also developed to improve the fit of batch tests for kinetic parameter identification. The results are encouraging: the iterative procedure significantly reduced the Theil’s Inequality Coefficient (TIC), used to evaluate the goodness of fit of the model for alkalinity, total volatile fatty acids, pH, COD, volatile solids, and ammoniacal nitrogen. Improvements in the TIC values, compared to the first iteration, ranged between 30 and 58%.
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Hetling, Leo J., Norbert A. Jaworski, and David J. Garretson. "Comparison of nutrient input loading and riverine export fluxes in large watersheds." Water Science and Technology 39, no. 12 (June 1, 1999): 189–96. http://dx.doi.org/10.2166/wst.1999.0546.
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An input-output mass balance watershed model was developed and tested on ten large benchmark watersheds in the northeastern United States. Inputs of chlorides, sulfates, potassium, phosphorus, and nitrogen were calculated from published census data on population, wastewater discharges, land use, air emissions, agriculture, forestry, and transportation. Attenuation factors were selected for the inputs, (for example, air deposition, fertilizer, point source discharges) and the average annual riverine export flux for the watersheds was calculated for the period from 1900 to 1995. Historic chlorides, sulfates, potassium, phosphorus and nitrogen river export fluxes were independently calculated for each watershed using long term monitoring data obtained primarily from the USGS and public water supplies. A comparison of the attenuated watershed inputs and the monitored output river flux suggests that it is possible to obtain reasonable estimates of watershed export fluxes from existing landscape input data. The above methodology was applied to two major tributaries of the Hudson River and the results are described. Input-output models which can simulate long term historical changes in riverine fluxes based on inputs rather than on land use types will be useful for the development of effective and efficient nutrient control programs on a watershed basis.
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Morais, Yasmim Yathiara Gomes Araújo, Fernando José Freire, Rosival Barros de Andrade Lima, Edilane Alice de Alcântara Assunção, Shyrlaine Lilian Moura Leão, and Lidiana Nayara Ralph. "STEMFLOW NUTRIENT INPUT IN TREES IN A TROPICAL FOREST IN PERNAMBUCO, BRAZIL." FLORESTA 51, no. 3 (June 22, 2021): 604. http://dx.doi.org/10.5380/rf.v51i3.71464.
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Rainfall is the main source of water in forest ecosystems and stemflow is an important pathway for nutrients to enter these ecosystems. Thus, this study aimed to evaluate effective precipitation in a fragment of tropical forest and stemflow nutrient input of tree species in different periods of rainfall. Total precipitation and throughfall were measured using rain gauges inside and at the edge of the fragment. After a phytosociological survey, nine species with the highest absolute density in the fragment were chosen and three individuals were selected. Water collectors were fixed around their trunk to collect stemflow water. The stemflow water was measured in milliliters, and pH, electrical conductivity and the input of K, P and Na were determined. Based on the throughfall and stemflow, the effective precipitation was calculated. The stemflow nutrient input presented the following decreasing order: Na>K>P. The high input of Na can be explained by the fact that the fragment is close to the coastal area. Stemflow of forest species proved to be an important pathway for nutrients to enter forest ecosystems, effectively participating in nutrient cycling.
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Melesse, Assefa M., Khabat Khosravi, John P. Tiefenbacher, Salim Heddam, Sungwon Kim, Amir Mosavi, and Binh Thai Pham. "River Water Salinity Prediction Using Hybrid Machine Learning Models." Water 12, no. 10 (October 21, 2020): 2951. http://dx.doi.org/10.3390/w12102951.
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Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual—M5 Prime (M5P) and random forest (RF)—and eight novel hybrid algorithms—bagging-M5P, bagging-RF, random subspace (RS)-M5P, RS-RF, random committee (RC)-M5P, RC-RF, additive regression (AR)-M5P, and AR-RF—to predict EC. Thirty-six years of observations collected by the Mazandaran Regional Water Authority were randomly divided into two sets: 70% from the period 1980 to 2008 was used as model-training data and 30% from 2009 to 2016 was used as testing data to validate the models. Several water quality variables—pH, HCO3−, Cl−, SO42−, Na+, Mg2+, Ca2+, river discharge (Q), and total dissolved solids (TDS)—were modeling inputs. Using EC and the correlation coefficients (CC) of the water quality variables, a set of nine input combinations were established. TDS, the most effective input variable, had the highest EC-CC (r = 0.91), and it was also determined to be the most important input variable among the input combinations. All models were trained and each model’s prediction power was evaluated with the testing data. Several quantitative criteria and visual comparisons were used to evaluate modeling capabilities. Results indicate that, in most cases, hybrid algorithms enhance individual algorithms’ predictive powers. The AR algorithm enhanced both M5P and RF predictions better than bagging, RS, and RC. M5P performed better than RF. Further, AR-M5P outperformed all other algorithms (R2 = 0.995, RMSE = 8.90 μs/cm, MAE = 6.20 μs/cm, NSE = 0.994 and PBIAS = −0.042). The hybridization of machine learning methods has significantly improved model performance to capture maximum salinity values, which is essential in water resource management.
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Iqbal, Mazhar, Md Rowshon Kamal, Mohd Fazly M., Hasfalina Che Man, and Aimrun Wayayok. "HYDRUS-1D Simulation of Soil Water Dynamics for Sweet Corn under Tropical Rainfed Condition." Applied Sciences 10, no. 4 (February 11, 2020): 1219. http://dx.doi.org/10.3390/app10041219.
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Assessment of soil water balance is essential to understand water dynamics for optimal use of water and fertilizers. The study intended to simulate soil water dynamics in sweet corn production under tropical rainfed conditions. Surface runoff, subsurface leaching, and evapotranspiration are the main components of water balance, especially in tropical environments. Therefore, intensive field experiments and HYDRUS-1D numerical modeling were applied to investigate the water balance components and analyzing water dynamics. The study was carried out in a sweet corn field for two growing seasons under the rainfed conditions at the Malaysian Agricultural Research and Development Institute (MARDI), Serdang, Malaysia. The total water inputs during the first and second seasons were 75.8 cm and 79.7 cm, respectively. Simulated results of evapotranspiration (ET) accounted for 40.7% and 33.1% of total water input during the first and second seasons. Surface runoff accounted for 41% and 28.6% in the first and second season, respectively. Water leaching accounted for 10.6%–26.8% of total water input during both seasons respectively. As rainfall fulfilled the crop water requirement throughout the growing seasons no additional irrigation was required. The overall simulation results validate the HYDRUS-1D as an effective tool to simulate soil water dynamics under rainfed conditions.
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Braguglia, C. M., G. Mininni, and A. Gianico. "Is sonication effective to improve biogas production and solids reduction in excess sludge digestion?" Water Science and Technology 57, no. 4 (March 1, 2008): 479–83. http://dx.doi.org/10.2166/wst.2008.003.
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Results of three semi-continuous anaerobic tests were reported and discussed. Each test was carried out by two parallel anaerobic reactors fed with waste activated sludge, either as it was sampled from the sewage treatment plant of Rome North or previously disintegrated by ultra-sound treatment. Activated sludge was sonicated at the energy input of 5,000 or 2,500 kJ kg−1 dry solids corresponding to a disintegration degree of approximately 8 or 4%, respectively. Sonication proved to be effective both in increasing VS destruction and cumulative biogas production. The best increase of VS destruction (from 30 to 35%) was achieved in test #3 carried out at high organic load (10 d residence time) and low energy input (2,500 kJ kg−1 dry solids). The best increase in cumulative biogas production (from 472 to 640 NL after 67 d of tests i.e.) was obtained in test #1 at low organic load (20 d residence time) and high energy input (5,000 kJ kg−1 dry solids). Specific biogas production varied in the tests carried out with untreated sludge (0.55 – 0.67 Nm3 kg−1 VS destroyed) but was practically unchanged for all the tests with sonicated sludge (0.7 Nm3 kg−1 VS destroyed).
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Adhikary, Sajal Kumar, Nitin Muttil, and Abdullah Gokhan Yilmaz. "Improving streamflow forecast using optimal rain gauge network-based input to artificial neural network models." Hydrology Research 49, no. 5 (December 5, 2017): 1559–77. http://dx.doi.org/10.2166/nh.2017.108.
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Abstract Accurate streamflow forecasting is of great importance for the effective management of water resources systems. In this study, an improved streamflow forecasting approach using the optimal rain gauge network-based input to artificial neural network (ANN) models is proposed and demonstrated through a case study (the Middle Yarra River catchment in Victoria, Australia). First, the optimal rain gauge network is established based on the current rain gauge network in the catchment. Rainfall data from the optimal and current rain gauge networks together with streamflow observations are used as the input to train the ANN. Then, the best subset of significant input variables relating to streamflow at the catchment outlet is identified by the trained ANN. Finally, one-day-ahead streamflow forecasting is carried out using ANN models formulated based on the selected input variables for each rain gauge network. The results indicate that the optimal rain gauge network-based input to ANN models gives the best streamflow forecasting results for the training, validation and testing phases in terms of various performance evaluation measures. Overall, the study concludes that the proposed approach is highly effective to achieve the enhanced streamflow forecasting and could be a viable option for streamflow forecasting in other catchments.
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Cutler, Paul M. "Modelling the evolution of subglacial tunnels due to varying water input." Journal of Glaciology 44, no. 148 (1998): 485–97. http://dx.doi.org/10.3189/s002214300000201x.
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AbstractThe time evolution of a subglacial tunnel cross-section is examined usine a two-dimensional finite-element ice-flow model coupled to an idealized drainage system. Simulations are driven by physically based calculations of surface water-input variations at Slorgiaciaren, Sweden. Highlights of the model are its ability to handle unsteady conditions and irregular tunnel shapes. Agreement between modelled water pressure and borehole water levels is good. The following conclusions are reached: (i) Tunnels adapt to fluctuating inflow on time-scales of days. Storms, during which effective pressure ranges from 0 to 0.9 MPa, cause significant adjustments but daily fluctuations due solely to melt-water inflow are minor, (ii) Open-channel flow may become commonplace late in the ablation season, (iii) Initial tunnel shape influences subsequent tunnel evolution and seasonal water-pressure variation. Over the course of a summer, tunnels retain some of their initial shape, though in all experiments the width-to-height ratio increased with time, (iv) Tunnel contraction forms broad low tunnels. However, (v) given two tunnels of equal initial area, the higher narrower one expands more rapidly. Thus, more semi-circular tunnels may capture How from broader neighbours early in the summer.
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Cutler, Paul M. "Modelling the evolution of subglacial tunnels due to varying water input." Journal of Glaciology 44, no. 148 (1998): 485–97. http://dx.doi.org/10.1017/s002214300000201x.
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AbstractThe time evolution of a subglacial tunnel cross-section is examined usine a two-dimensional finite-element ice-flow model coupled to an idealized drainage system. Simulations are driven by physically based calculations of surface water-input variations at Slorgiaciaren, Sweden. Highlights of the model are its ability to handle unsteady conditions and irregular tunnel shapes. Agreement between modelled water pressure and borehole water levels is good. The following conclusions are reached: (i) Tunnels adapt to fluctuating inflow on time-scales of days. Storms, during which effective pressure ranges from 0 to 0.9 MPa, cause significant adjustments but daily fluctuations due solely to melt-water inflow are minor, (ii) Open-channel flow may become commonplace late in the ablation season, (iii) Initial tunnel shape influences subsequent tunnel evolution and seasonal water-pressure variation. Over the course of a summer, tunnels retain some of their initial shape, though in all experiments the width-to-height ratio increased with time, (iv) Tunnel contraction forms broad low tunnels. However, (v) given two tunnels of equal initial area, the higher narrower one expands more rapidly. Thus, more semi-circular tunnels may capture How from broader neighbours early in the summer.
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Fang, Yujian, Shouqi Yuan, Jinfeng Zhang, Yimeng Weng, and Jianrui Liu. "Utilizing siphon for effective management of air in gravity-fed water pipelines." Canadian Journal of Civil Engineering 45, no. 7 (July 2018): 559–69. http://dx.doi.org/10.1139/cjce-2017-0490.
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A novel siphon design has been invented and continuously optimized by Mr. Weng to adapt various pipe diameters since 1990. Within the past 30 years of practical experience, although its success is in increasing transmission capacity, delivering water to higher elevation, and longer distance without additional power input has drawn wide social attention. But this technology has not obtained the industrial acceptance so far due to the lack of theoretical knowledge to explain how the head loss is reduced significantly. This paper presents the experimental findings in the laboratory from an undulating water pipeline with a novel siphon inlet, and reveals the excellence of this design for air management in the pipeline. Together with the experimental findings and relevant literature, two engineering projects utilizing this novel siphon are investigated in detail to illustrate its superior hydraulic performance.
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Cui, Zhen, Yanlai Zhou, Shenglian Guo, Jun Wang, and Chong-Yu Xu. "Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure." Journal of Hydrology 609 (June 2022): 127764. http://dx.doi.org/10.1016/j.jhydrol.2022.127764.
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Martini, Luiz Carlos Pittol. "Sensitivity analysis of the AquaCrop parameters for rainfed corn in the South of Brazil." Pesquisa Agropecuária Brasileira 53, no. 8 (August 2018): 934–42. http://dx.doi.org/10.1590/s0100-204x2018000800008.
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Abstract: The objective of this work was to perform a sensitivity analysis of the main input parameters required for the AquaCrop water balance model, using biomass and grain yield data of a rainfed-simulated corn crop, obtained along the climate data series of 1987-2016 in the South of Brazil. The levels of soil-water stress and the depths of maximum effective rooting were the input parameters that most affected the biomass and grain yields simulated by the model, followed by the crop coefficient, water-use efficiency, soil water storage capacity, and contribution of groundwater to water availability in the root zone. The parameters crop cycle duration, plant density, pattern of soil-water extraction, and field surface practices showed little or no impact on the final results. AquaCrop is a robust water balance model, with small or moderate general sensitivity to variations of the main input parameter values, which makes it applicable to situations with field data limitations.
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Wu, Xia, Lucy Marshall, and Ashish Sharma. "Quantifying input uncertainty in the calibration of water quality models: reordering errors via the secant method." Hydrology and Earth System Sciences 26, no. 5 (March 4, 2022): 1203–21. http://dx.doi.org/10.5194/hess-26-1203-2022.
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Abstract. Uncertainty in input can significantly impair parameter estimation in water quality modeling, necessitating the accurate quantification of input errors. However, decomposing the input error from the model residual error is still challenging. This study develops a new algorithm, referred to as the Bayesian Error Analysis with Reordering (BEAR), to address this problem. The basic approach requires sampling errors from a pre-estimated error distribution and then reordering them with their inferred ranks via the secant method. This approach is demonstrated in the case of total suspended solids (TSSs) simulation via a conceptual water quality model. Based on case studies using synthetic data, the BEAR method successfully improves the input error identification and parameter estimation by introducing the error rank estimation and the error position reordering. The results of a real case study demonstrate that, even with the presence of model structural error and output data error, the BEAR method can approximate the true input and bring a better model fit through an effective input modification. However, its effectiveness depends on the accuracy and selection of the input error model. The application of the BEAR method in TSS simulation can be extended to other water quality models.
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Septiano, Jalest, Amega Yasutra, and Silvya Dewi Rahmawati. "Build of Machine Learning Proxy Model for Prediction of Wax Deposition Rate in Two Phase Flow Water-Oil." Scientific Contributions Oil and Gas 45, no. 1 (April 1, 2022): 34–48. http://dx.doi.org/10.29017/scog.45.1.922.
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Wax deposit is one of the major fl ow assurance experienced in the process of oil production and transportation from sub- surface to surface. Large amounts of data are required to perform modeling using existing thermodynamic models such as carbon number data from HGTC. In this paper, a machine learning algorithm using unifi ed model approach from Huang (2008). Two types of input are implemented in order to simulate infl uence of feature selection used in training and testing machine learning which are input A consists of water volume fraction (fw), shear stress (τw), effective viscosity (μe), wax concentration gradient (dC/dT), and temperature gradient (dT/dR) and input B consists of water volume fraction (fw), shear stress (τw), effective viscosity (μe), wax concentration gradient (dC/dT), temperature gradient (dT/dR), shear stripping variable (SV) dan diffusion variable (DV). The random forest with Ntree = 500 known to be the best machine learning method compared to others. Based on accuracy parameter it achieves error parameter R-squared (R2) for training, testing and total data for input A and B are 0.999, 0.992, 0.9975 and 0.999, 0.993, 0.9977, respectively.
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Wei, Xin-Jiang, Xiao Wang, Gang Wei, Cheng-Wei Zhu, and Yu Shi. "Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic Models." Journal of Marine Science and Engineering 9, no. 1 (January 12, 2021): 71. http://dx.doi.org/10.3390/jmse9010071.
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The vertical tunneling method is an emerging technique to build sewage inlets or outlets in constructed horizontal tunnels. The jacking force used to drive the standpipes upward is an essential factor during the construction process. This study aims to predict the jacking forces during the vertical tunneling construction process through two intelligence systems, namely, artificial neural networks (ANNs) and hybrid genetic algorithm optimized ANNs (GA-ANNs). In this paper, the Beihai hydraulic tunnel constructed by the vertical tunneling method in China is introduced, and the direct shear tests have been conducted. A database composed of 546 datasets with ten inputs and one output was prepared. The effective parameters are classified into three categories, including tunnel geometry factors, the geological factor, and jacking operation factors. These factors are considered as input parameters. The tunnel geometry factors include the jacking distance, the thickness of overlaying soil, and the height of overlaying water; the geological factor refers to the geological conditions; and the jacking operation factors consist of the dead weight of standpipes, effective overburden soil pressure, effective lateral soil pressure, average jacking speed, construction hours, and soil weakening measure. The output parameter, on the other hand, refers to the jacking force. Performance indices, including the coefficient of determination (R2), root mean square error (RMSE), and the absolute value of relative error (RE), are computed to compare the performance of the ANN models and the GA-ANN models. Comparison results show that the GA-ANN models perform better than the ANN model, especially on the RMSE values. Finally, parametric sensitivity analysis between the input parameters and output parameter is conducted, reaching the result that the height of overlaying water, the average jacking speed, and the geological condition are the most effective input parameters on the jacking force in this study.
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Veer, Saurabh, Saurabh Khandve, Yash Pawar, Kushal Marale, and Prof Ashwini Waghule. "Design Water Supply Network Using Epanet Software." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 108–12. http://dx.doi.org/10.22214/ijraset.2022.47828.
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Abstract: This study represents use of EPANET software in the design of water distribution network. EPANET software is a user friendly software. In order to ensure the availability of sufficient quantity of good quality of water to the various section of community in accordance with the demand, many computer tools were developed, out of all the tools available EPANET become most popular and convenient for the effective design of complex pipe networks. This paper highlights only the effective design and distribution of network of pipes using EPANET tool. The residual head at each and every node was found out by having the elevation as input and thereby the corresponding flow quantities were derived like residual head, velocity and nodal demand etc.
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Dvorkin, Jack, Gary Mavko, and Boris Gurevich. "Fluid substitution in shaley sediment using effective porosity." GEOPHYSICS 72, no. 3 (May 2007): O1—O8. http://dx.doi.org/10.1190/1.2565256.
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The traditional method of fluid substitution in porous rock requires the total porosity and the elastic modulus of the mineral phase as input and assumes that the fluid reaches instantaneous hydraulic equilibrium throughout the pore space. This assumption may not be appropriate for shaley sediment because of the low permeability of shale and the resulting immobility of the water in it. To address this problem, we propose an alternative method that uses effective porosity instead of total porosity. Effective porosity is lower than total porosity if porous shale is present in the system. A new, composite mineral phase is introduced, which includes the porous water-saturated shale together with the nonporous minerals and whose elastic modulus is an average of those of its components, including the porous shale. This alternative method increases the sensitivity of the elastic properties of sediment-to-pore-fluid changes and therefore may be used as a physics-based theoretical tool to better explain and interpret seismic data during exploration as well as variations in seismic response as hydrocarbon production progresses.
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Yang, Jun Jie, Zhi Jiang Zuo, and Wu Xin Yu. "Experimental Investigation on Air-Aided Water EDM." Advanced Materials Research 148-149 (October 2010): 471–74. http://dx.doi.org/10.4028/www.scientific.net/amr.148-149.471.
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Experiments for a new type of electric discharge machining (EDM) were conducted with the water film dielectric formed by tap water and compressed air. An energy factor E is introduced and employed to estimate unit-area effective power input. The experiments show that the flushing conditions with lower air jet pressure can get lower relative tool wear and higher material removal rate (MRR). Higher discharge current and higher energy factor E obtain the higher MRR in air-aided water EDM with positive polarity tool.
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Vallet, A., C. Bertrand, and J. Mudry. "Effective rainfall: a significant parameter to improve understanding of deep-seated rainfall triggering landslide – a simple computation temperature based method applied to Séchilienne unstable slope (French Alps)." Hydrology and Earth System Sciences Discussions 10, no. 7 (July 10, 2013): 8945–91. http://dx.doi.org/10.5194/hessd-10-8945-2013.
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Abstract. Pore water pressure, build up by recharge of hydrosystems, is one of the main triggering factors of deep seated landslides. Effective rainfall, which is the part of the rainfall which recharges the aquifer, is a significant parameter. Soil-water balance is an accurate way to estimate effective rainfall. Nevertheless this approach requires evapotranspiration, soil water storage and runoff characterization. Available soil storage and runoff were deduced from field observations whereas evapotranspiration computation is a highly demanding method requiring significant input of meteorological data. Most of the landslide sites used weather stations with limited datasets. A workflow method was developed to compute effective rainfall requiring only temperature and rainfall as inputs. Two solar radiation and five commonly used evapotranspiration equations were tested at Séchilienne. The method was developed to be as general as possible in order to be able to be applied to other landslides. This study demonstrated that, for the Séchilienne unstable slope, the displacement data correlation performance (coefficient of determination) is significantly enhanced with effective rainfall (0.633) compared to results obtained with raw rainfall (0.436) data. The proposed method for estimation of effective rainfall was developed to be sufficiently simple to be used by any non-hydro specialist who intends to characterize the relationship of rainfall to landslide displacements.
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Ren, Shu Yan, and Le Peng Song. "Fire Water Supply Control System of Petrochemical Enterprises." Advanced Materials Research 846-847 (November 2013): 335–38. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.335.
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The petroleum chemical industry in the production process, fire water supply control system can not meet the normal transmission of signal nonlinear, reliability is low. Compared with the conventional fuzzy PID control system of PID water supply system, can be effective for the nonlinear input signal, and faster than the reaction rate of PID control, the more reliable. Application of fuzzy control to the fire water supply system of conventional, enhanced fire system speediness, stability and reliability.
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Khokhani, Aayush, Prince Kalavadiya, Ravindra Taviya, Zeel Morker, and Prof Joseph Sibi. "A Review for an Effective Approach towards Hydroelectric Power Generation Using In-Pipe Mesoscale Submersible Turbine." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 1260–74. http://dx.doi.org/10.22214/ijraset.2022.40859.
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Abstract: Turbines are playing a massive role in our day-to-day lives in the back-end portion of lifestyles where they have been efficiently providing us energy through tidal, hydrological, and many such other mediums. A dominant explanation for the need for energy production has been introduced in the 1800s since the requirement for a high consumption of energy in various forms has started taking place. A very common method that has been observed in today’s innovative mannerism is the use of turbines in dams, undersea, elsewhere at locations where the flow of fluid induces better outputs. Vertical axis turbines, Francis turbines as well as Kaplan turbines have frequently opted for such purposes but after studying over 65 works done by adepts, professionals, and experts; the purposely implemented input that is required to fulfill the output doesn’t have to always be a necessity, it seemed to be the new designing restructured platform for users as well as providers. An overture to install micro versioned turbines of macro hydroelectric power plants within a residential or commercial structure at the main water-supply connections either at their junctions or directly near the overhead water tanks cannot just provide subtle but fortifying and tireless inputs since the flow of water will be anticipated naturally by the implicated outcomes through day-to-day chores performed. Hydrokinetic conversion systems may appear suitable in harvesting energy from such renewable resources, despite the fact that they are still in the early stages of development. Contrary to what has been assumed, there are numerous possibilities for the utilization of this energy for common areas/public zones such as signals, street lights, or any such productive amenities to bestow leading-edge facilities without any hitch regarding external contriving inputs. Keywords: Turbine, CFD Simulations, Archimedes screw, Water distribution logistics, Mini-Hydro-Power Plant, In-Pipe electricity generation, Inline hydroelectric generation.
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Manohar, Krishpersad, Anthony Ademola Adeyanju, and Kureem Vialva. "Performance characteristics of a small water-hammer head pump." Drinking Water Engineering and Science 12, no. 2 (November 5, 2019): 59–64. http://dx.doi.org/10.5194/dwes-12-59-2019.
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Abstract. Many rural farming areas are located far from a reliable electricity supply; hence, obtaining a reliable source of water for crops and livestock can prove to be an expensive venture. A water pump operating on the water-hammer effect requires no external power source and can serve as an effective means of pumping water to a higher altitude once a reliable supply is available. A low-cost small water-hammer head pump was designed to operate on the water-hammer head effect created by the sudden stoppage of a flowing fluid. This design consisted of an inlet section followed by the pump body, a pressure section and an outlet. The experimental set-up for testing the water-hammer head pump was designed with a variable head input and an adjustable head output. For each test configuration, a total of 10 samples of pump supply water and pump exhausted water were collected. The water samples were collected for 30 s in each case. The results showed a non-linear variation of water flow with respect to pump outlet height. The pump was capable of delivering water to a maximum height of 8 to 10 times the height of the input head. The pump operated at average efficiencies of 26 %, 16 % and 6 % when the delivery height was 2, 4 and 6 times the input head height, respectively. There was a 5 % incremental decrease in pump efficiency as the delivery height increased in increments of the corresponding input head height.
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Zhu, Chuan-Yong, Zhi-Yang He, Mu Du, Liang Gong, and Xinyu Wang. "Predicting the effective thermal conductivity of unfrozen soils with various water contents based on artificial neural network." Nanotechnology 33, no. 6 (November 19, 2021): 065408. http://dx.doi.org/10.1088/1361-6528/ac3688.
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Abstract The effective thermal conductivity of soils is a crucial parameter for many applications such as geothermal engineering, environmental science, and agriculture and engineering. However, it is pretty challenging to accurately determine it due to soils’ complex structure and components. In the present study, the influences of different parameters, including silt content (m si), sand content (m sa), clay content (m cl), quartz content (m qu), porosity, and water content on the effective thermal conductivity of soils, were firstly analyzed by the Pearson correlation coefficient. Then different artificial neural network (ANN) models were developed based on the 465 groups of thermal conductivity of unfrozen soils collected from the literature to predict the effective thermal conductivity of soils. Results reveal that the parameters of m si, m sa, m cl, and m qu have a relatively slight influence on the effective thermal conductivity of soils compared to the water content and porosity. Although the ANN model with six parameters has the highest accuracy, the ANN model with two input parameters (porosity and water content) could predict the effective thermal conductivity well with acceptable accuracy and R 2 = 0.940. Finally, a correlation of the effective thermal conductivity for different soils was proposed based on the large number of results predicted by the two input parameters ANN-based model. This correlation has proved to have a higher accuracy without assumptions and uncertain parameters when compared to several commonly used existing models.
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Ledergerber, J. M., T. Maruéjouls, and P. A. Vanrolleghem. "No-regret selection of effective control handles for integrated urban wastewater systems management under parameter and input uncertainty." Water Science and Technology 81, no. 8 (April 1, 2020): 1749–56. http://dx.doi.org/10.2166/wst.2020.144.
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Abstract Regulatory water quality limits are extended from the wastewater resource recovery facility (WRRF) to the sewer system. It is thus necessary to properly integrate those systems for the evaluation of the overall emissions to the receiving water. The integration of the sewer system and the WRRF, however, leaves us with multiple potential options to reduce these emissions. The proposed approach builds on previous research using global sensitivity analysis (GSA) as a screening method for available control handles. It considers parameter and input uncertainty to select control handles that generate large benefits even if the model differs from reality. Results from a real-life case study indicate that the three top-rated handles are comparably effective for all considered uncertainty and variability scenarios. But the results also showed that this does not apply to lower-rated handles.
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Oubennaceur, Khalid, Karem Chokmani, Miroslav Nastev, Yves Gauthier, Jimmy Poulin, Marion Tanguy, Sebastien Raymond, and Rachid Lhissou. "New Sensitivity Indices of a 2D Flood Inundation Model Using Gauss Quadrature Sampling." Geosciences 9, no. 5 (May 14, 2019): 220. http://dx.doi.org/10.3390/geosciences9050220.
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A new method for sensitivity analysis of water depths is presented based on a two-dimensional hydraulic model as a convenient and cost-effective alternative to Monte Carlo simulations. The method involves perturbation of the probability distribution of input variables. A relative sensitivity index is calculated for each variable, using the Gauss quadrature sampling, thus limiting the number of runs of the hydraulic model. The variable-related highest variation of the expected water depths is considered to be the most influential. The proposed method proved particularly efficient, requiring less information to describe model inputs and fewer model executions to calculate the sensitivity index. It was tested over a 45 km long reach of the Richelieu River, Canada. A 2D hydraulic model was used to solve the shallow water equations (SWE). Three input variables were considered: Flow rate, Manning’s coefficient, and topography of a shoal within the considered reach. Four flow scenarios were simulated with discharge rates of 759, 824, 936, and 1113 m 3 / s . The results show that the predicted water depths were most sensitive to the topography of the shoal, whereas the sensitivity indices of Manning’s coefficient and the flow rate were comparatively lower. These results are important for making better hydraulic models, taking into account the sensitivity analysis.
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Terzi, Özlem. "Monthly Rainfall Estimation Using Data-Mining Process." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/698071.
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It is important to accurately estimate rainfall for effective use of water resources and optimal planning of water structures. For this purpose, the models were developed to estimate rainfall in Isparta using the data-mining process. The different input combinations having 1-, 2-, 3- and 4-input parameters were tried using the rainfall values of Senirkent, Uluborlu, Eğirdir, and Yalvaç stations in Isparta. The most appropriate algorithm was determined as multilinear regression among the models developed with various data-mining algorithms. The input parameters of Multilinear Regression model were the monthly rainfall values of Senirkent, Uluborlu and Eğirdir stations. The relative error of this model was calculated as 0.7%. It was shown that the data mining process can be used in estimation of missing rainfall values.
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Vuolo, F., L. Essl, L. Zappa, T. Sandén, and H. Spiegel. "Water and nutrient management: the Austria case study of the FATIMA H2020 project." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 400–405. http://dx.doi.org/10.1017/s2040470017000541.
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The project “FArming Tools for external nutrient Inputs and water Management” (FATIMA, H2020-SFS2) is developing satellite-based methodologies and information to support effective and efficient water and nitrogen input recommendations in agricultural production. This paper focuses on nitrogen recommendation for winter cereals in Austria and presents preliminary findings from the 2015/16 crop growing season. The Nitrogen Nutrition Index was applied using an empirical relationship to derive dry mass from Leaf Area Index (LAI) and %Na from a chlorophyll index. Results showed a very high correlation between LAI and above ground dry mass (R2=0.95) but a lower correlation between the chlorophyll index and %Na (R2=0.24). Despite various indices tested, the relationship to estimate %Na remains weak. Additional field data and research are needed to further study this aspect.
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Luca, Davide Luciano De, and Andrea Petroselli. "Advances in Modelling of Rainfall Fields." Hydrology 9, no. 8 (August 10, 2022): 142. http://dx.doi.org/10.3390/hydrology9080142.
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Rainfall is the main input for all hydrological models, such as rainfall–runoff models and the forecasting of landslides triggered by precipitation, with its comprehension being clearly essential for effective water resource management as well [...]
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Adnan, Norliyana, Mohd Parid Mamat, and Tuan Marina Tuan Ibrahim. "Assessing value of water purification services by Kelantan Forest Reserves." IOP Conference Series: Earth and Environmental Science 1102, no. 1 (November 1, 2022): 012062. http://dx.doi.org/10.1088/1755-1315/1102/1/012062.
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Forest acts as natural water purification agent that is very effective in providing clean water resources to consumers, especially for domestic use. Clean water from natural purification processes would lead to a reduction in water treatment costs at treatment plants. This service is important for safety, assurance of resource availability and health. Hence, the aim of this paper is to assess the value of purification services by Kelantan forest reserves. The assessment is based on economic benefit using the benefit transfer approach of cost functions model with input for environmental values. Assessment enclosed for Kelantan main basin, namely Pergau, Lebir and Galas Basin. Water Treatment Plants (WTPs) data and forest land use information are used as the main data input for the analysis. Results show that basins with more virgin forest have lower treatment cost reduction in WTPs. Hence, the conservation of forest reserves as water catchment areas is important to ensure the availability of clean water especially for domestic uses.
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Asadi, Haniyeh, Mohammad T. Dastorani, Khabat Khosravi, and Roy C. Sidle. "Applying the C-Factor of the RUSLE Model to Improve the Prediction of Suspended Sediment Concentration Using Smart Data-Driven Models." Water 14, no. 19 (September 24, 2022): 3011. http://dx.doi.org/10.3390/w14193011.
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The accurate forecasts and estimations of the amount of sediment transported by rivers are critical concerns in water resource management and soil and water conservation. The identification of appropriate and applicable models or improvements in existing approaches is needed to accurately estimate the suspended sediment concentration (SSC). In recent decades, the utilization of intelligent models has substantially improved SSC estimation. The identification of beneficial and proper input parameters can greatly improve the performance of these smart models. In this regard, we assessed the C-factor of the revised universal soil loss equation (RUSLE) as a new input along with hydrological variables for modeling SSC. Four data-driven models (feed-forward neural network (FFNN); support vector regression (SVR); adaptive neuro-fuzzy inference system (ANFIS); and radial basis function (RBF)) were applied in the Boostan Dam Watershed, Iran. The cross-correlation function (CCF) and partial autocorrelation function (PAFC) approaches were applied to determine the effective lag times of the flow rate and suspended sediment, respectively. Additionally, several input scenarios were constructed, and finally, the best input combination and model were identified through trial and error and standard statistics (coefficient of determination (R2); root mean square error (RMSE); mean absolute error (MAE); and Nash–Sutcliffe efficiency coefficient (NS)). Our findings revealed that using the C-factor can considerably improve model efficiency. The best input scenario in which the C-factor was combined with hydrological data improved the NS by 16.4%, 21.4%, 0.17.5%, and 23.2% for SVR, ANFIS, FFNN, and RBF models, respectively, compared with the models using only hydrological inputs. Additionally, a comparison among the different models showed that the SVR model had about 4.1%, 13.7%, and 23.3% (based on the NS metric) higher accuracy than ANFIS, FFNN, and RBF for SSC estimation, respectively. Thus, the SVR model using hydrological data along with the C-factor can be a cost-effective and promising tool in SSC prediction at the watershed scale.
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Arnold, J. G., R. Srinivasan, T. S. Ramanarayanan, and M. DiLuzio. "Water resources of the Texas Gulf Basin." Water Science and Technology 39, no. 3 (February 1, 1999): 121–33. http://dx.doi.org/10.2166/wst.1999.0151.
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A geographic information system (GIS) has been integrated with a distributed parameter, continuous time, nonpoint source pollution model SWAT (Soil and Water Assessment Tool) for the management of water resources. This integration has proven to be effective and efficient for data collection and to visualize and analyze the input and output of simulation models. The SWAT-GIS system is being used to model the hydrology of eighteen major river systems in the United States (HUMUS). This paper focuses on the integration of SWAT (basin scale hydrologic model) with the Geographical Resources Analysis Support System (GRASS-GIS) and a relational database management system. The system is then applied to the Texas Gulf River basin. Input data layers (soils, land use, and elevation) were collected at a scale of 1:250,000 from various sources. Average monthly simulated and observed stream flow records from 1970-1979 are presented for the hydrologic cataloging units (HCU) defined by the United States Geological Survey (USGS) in the Texas Gulf basin. Average annual sediment yields computed from sediment rating curves are compared against simulated sediment yields from seven river basins within the Texas Gulf showing reasonable agreement.
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Kleidorfer, M., A. Deletic, T. D. Fletcher, and W. Rauch. "Impact of input data uncertainties on urban stormwater model parameters." Water Science and Technology 60, no. 6 (September 1, 2009): 1545–54. http://dx.doi.org/10.2166/wst.2009.493.
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The use of urban drainage models requires careful calibration, where model parameters are selected in order to minimize the difference between measured and simulated results. It has been recognized that often more than one set of calibration parameters can achieve similar model accuracy. A probability distribution of model parameters should therefore be constructed to examine the model's sensitivity to its parameters. With increasing complexity of models, it also becomes important to analyze the model parameter sensitivity while taking into account uncertainties in input and calibration data. In this study a Bayesian approach was used to develop a framework for quantification of impacts of uncertainties in the model inputs on the parameters of a simple integrated stormwater model for calculating runoff, total suspended solids and total nitrogen loads. The framework was applied to two catchments in Australia. It was found that only systematic rainfall errors have a significant impact on flow model parameters. The most sensitive flow parameter was the effective impervious area, which can be calibrated to completely compensate for the input data uncertainties. The pollution model parameters were influenced by both systematic and random rainfall errors. Additionally an impact of circumstances (e.g. catchment type, data availability) has been recognized.
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Yang, Yang, Shiwei Liu, Cunde Xiao, Cuiyang Feng, and Chenyu Li. "Evaluating Cryospheric Water Withdrawal and Virtual Water Flows in Tarim River Basin of China: An Input–Output Analysis." Sustainability 13, no. 14 (July 7, 2021): 7589. http://dx.doi.org/10.3390/su13147589.
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In Tarim River Basin (TRB), the retreat of glacier and snow cover reduction due to climate warming threatens the regional economy of downstream basins that critically depends on meltwater. However, the quantitative evaluation of its impact on multiple sectors of the socioeconomic system is incomplete. Based on compiled regional input–output table of the year 2012, this study developed a method to analyze the relationships between economic activities and related meltwater withdrawal, as well as sectoral transfer. The results show that the direct meltwater withdrawal intensity (DMWI) of agriculture was much higher than other sectors, reaching 2348.02 m3/10,000 CNY. Except for A01 (agriculture) and A02 (mining and washing of coal), the embodied meltwater withdrawal (EMW) driven by the final demand of other sectors was greater than direct meltwater withdrawal, and all sectors required inflows of virtual water (72.45 × 108 m3, accounting for 29% of total supply from cryospheric water resources) for their production processes in 2012. For sectors with high DMWI, improving water-use efficiency is an effective way to reduce water withdrawal. To some extent, the unbalanced supply of cryospheric water resources due to geographical segregation can be regulated by virtual water flows from water-saving to water-intensive sectors. Such decisions can affect the balance between socioeconomic development and environment conservation for long-term sustainability.
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Lin, Cherng-Yuan, and Lei Ma. "Effects of Water Removal from Palm Oil Reactant by Electrolysis on the Fuel Properties of Biodiesel." Processes 10, no. 1 (January 6, 2022): 115. http://dx.doi.org/10.3390/pr10010115.
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Biodiesel, which is composed of mono-alkyl esters of long carbon-chained fatty acids, is used as an alternative fuel to petro-diesel. The water content of the reactant mixture of feedstock oil influences the extent of transesterification and thus the fuel characteristics. Lower water content in feedstock oil is generally suggested for successful transesterification. This experimental study removed water from the reactant mixture of feedstock palm oil and methanol during transesterification using various systems composed of either electrodes or molecular sieves with rotary vibration. The effect of input electrical energy, number of electrodes, vibration modes, and operating time on the amount of water removed from the reactant mixture and the fuel properties of the final biodiesel product were analyzed and compared with those achieved using molecular sieves. The results show that the biodiesel—after water was removed during transesterification—appeared to have increased kinematic viscosity, cetane index, distillation temperature, and acid value, while the heating value, flash point, ignition point, and water content decreased with an increase in the input electrical energy of the electrodes responsible for electrolyzing water away. Electrolysis by the double-pair electrodes was more effective at reducing acid value and water content than that performed by the single-pair electrodes under the same input electrical energy. The biodiesel was found to have the lowest water content (0.0304 wt.%) and the highest water-removal rate (0.011 wt.%) when water was removed during transesterification by the double-pair electrodes with an input electrical energy of 9 J/(g palm oil). The water-removal rate of the rotary-vibrating molecular sieves was 11.24 times that of the single-pair electrodes. The biodiesel was found to have increased kinematic viscosity with higher input electrical energy, reaching 5.15 mm2/s when the double-pair electrodes with an input electrical energy of 11 J/(g palm oil) were used. Longer carbon-chained fatty acids, ranging from C20 to C24, amounted to 0.74 wt.% of the biodiesel produced using the double-pair electrodes, which was greater than that seen for the single-pair electrodes. However, the molecular sieve method consumed more energy than the double-pair electrodes did to remove the same amount of water from the palm oil reactant mixture via transesterification.
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Maslova, A. A., V. M. Panarin, K. V. Grishakov, N. A. Rybka, E. A. Kotova, and D. A. Selezneva. "Use of Artificial Neural Networks to Predict Levels of Air Pollution and Water Bodies." Ecology and Industry of Russia 23, no. 8 (August 13, 2019): 36–41. http://dx.doi.org/10.18412/1816-0395-2019-8-36-41.
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Describes the process of creating a simple and effective tool for predicting the quality of air and water bodies. Artificial neural networks are an effective tool for predicting the concentrations of suspended particles of heavy metals. The correct choice of input and output data with a clear relationship between them is necessary to obtain reliable results. Emphasis is placed on predictions of heavy metals due to permissible level of these pollutants, which often was exceeded in Tula. For given conditions, the best results are obtained using a single-layer perception with a back propagation algorithm.
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Vassos, Troy D. "Future Directions in Instrumentation, Control and Automation in the Water and Wastewater Industry." Water Science and Technology 28, no. 11-12 (December 1, 1993): 9–14. http://dx.doi.org/10.2166/wst.1993.0640.
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The need to optimize treatment plant performance and to meet increasingly stringent effluent criteria are two key factors affecting future development of instrumentation, control and automation (ICA) applications in the water and wastewater industry. Two case studies are presented which highlight the need for dynamic modelling and simulation software to assist operations staff in developing effective instrumentation control strategies, and to provide a training environment for the evaluation of such strategies. One of the limiting factors to date in realizing the potential benefits of ICA has been the inability to adequately interpret the large number of existing instrumentation inputs available at treatment facilities. The number of inputs can exceed the number of control loops by up to three orders of magnitude. The integration of dynamic modelling and expert system software is seen to facilitate the interpretation of real-time data, allowing both quantitative (instrumented) and qualitative (operator input) information to be integrated for process control. Improvements in sensor reliability and performance, and the development of biological monitoring sensors and control algorithms are also discussed.
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Steynberg, M. C., and A. Vermeulen. "IAFECT™: a management tool to ensure effective technology training in the drinking water industry." Water Supply 3, no. 1-2 (March 1, 2003): 449–54. http://dx.doi.org/10.2166/ws.2003.0137.
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For years training was evaluated with measures such as numbers of participants, number of programs, length of programs, cost of programs and content of programs. These input focused measures have to be replaced by output focused measures. The output focused measures include learning profile and whole brain approach for the learner, competency requirements for the job, management’s role before, during and after training as well as the competency of the trainer and the effectiveness of the training environment. However, to ensure that the highest possible scores for these measures can be achieved, a multidisciplinary approach is of paramount importance. The purpose of this article is to demonstrate the IAFECT™ management tool designed to ensure effective technology training. IAFECT™ is a systematic approach that involves all stakeholders. It focuses on technical competence and a high Return-On-Investment.
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Najah, A., A. El-Shafie, O. A. Karim, and O. Jaafar. "Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations." Hydrology and Earth System Sciences 15, no. 8 (August 29, 2011): 2693–708. http://dx.doi.org/10.5194/hess-15-2693-2011.
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Abstract. This study examined the potential of Multi-layer Perceptron Neural Network (MLP-NN) in predicting dissolved oxygen (DO) at Johor River Basin. The river water quality parameters were monitored regularly each month at four different stations by the Department of Environment (DOE) over a period of ten years, i.e. from 1998 to 2007. The following five water quality parameters were selected for the proposed MLP-NN modelling, namely; temperature (Temp), water pH, electrical conductivity (COND), nitrate (NO3) and ammonical nitrogen (NH3-NL). In this study, two scenarios were introduced; the first scenario (Scenario 1) was to establish the prediction model for DO at each station based on five input parameters, while the second scenario (Scenario 2) was to establish the prediction model for DO based on the five input parameters and DO predicted at previous station (upstream). The model needs to verify when output results and the observed values are close enough to satisfy the verification criteria. Therefore, in order to investigate the efficiency of the proposed model, the verification of MLP-NN based on collection of field data within duration 2009–2010 is presented. To evaluate the effect of input parameters on the model, the sensitivity analysis was adopted. It was found that the most effective inputs were oxygen-containing (NO3) and oxygen demand (NH3-NL). On the other hand, Temp and pH were found to be the least effective parameters, whereas COND contributed the lowest to the proposed model. In addition, 17 neurons were selected as the best number of neurons in the hidden layer for the MLP-NN architecture. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Coefficient of Efficiency (CE), Mean Square Error (MSE) and Coefficient of Correlation (CC). A relatively low correlation between the observed and predicted values in the testing data set was obtained in Scenario 1. In contrast, high coefficients of correlation were obtained between the observed and predicted values for the test sets of 0.98, 0.96 and 0.97 for all stations after adopting Scenario 2. It appeared that the results for Scenario 2 were more adequate than Scenario 1, with a significant improvement for all stations ranging from 4 % to 8 %.
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Najah, A. A., A. El-Shafie, O. A. Karim, and O. Jaafar. "Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations." Hydrology and Earth System Sciences Discussions 8, no. 3 (June 23, 2011): 6069–112. http://dx.doi.org/10.5194/hessd-8-6069-2011.
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Abstract. This study examined the potential of Multi-layer Perceptron Neural Network (MLP-NN) in predicting dissolved oxygen (DO) at Johor River Basin. The river water quality parameters were monitored regularly each month at four different stations by the Department of Environment (DOE) over a period of ten years, i.e. from 1998 to 2007. The following five water quality parameters were selected for the proposed MLP-NN modelling, namely; temperature (Temp), water pH, electrical conductivity (COND), nitrate (NO3) and ammonical nitrogen (NH3–NL). In this study, two scenarios were introduced; the first scenario (Scenario 1) was to establish the prediction model for DO at each station based on five input parameters, while the second scenario (Scenario 2) was to establish the prediction model for DO based on the five input parameters and DO predicted at previous station (upstream). The model needs to verify when output results and the observed values are close enough to satisfy the verification criteria. Therefore, in order to investigate the efficiency of the proposed model, the verification of MLP-NN based on collection of field data within duration 2009–2010 is presented. To evaluate the effect of input parameters on the model, the sensitivity analysis was adopted. It was found that the most effective inputs were oxygen-containing (NO3) and oxygen demand (NH3–NL). On the other hand, Temp and pH were found to be the least effective parameters, whereas COND contributed the lowest to the proposed model. In addition, 17 neurons were selected as the best number of neurons in the hidden layer for the MLP-NN architecture. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Coefficient of Efficiency (CE), Mean Square Error (MSE) and Coefficient of Correlation (CC). A relatively low correlation between the observed and predicted values in the testing data set was obtained in Scenario 1. In contrast, high coefficients of correlation were obtained between the observed and predicted values for the test sets of 0.98, 0.96 and 0.97 for all stations after adopting Scenario 2. It appeared that the results for Scenario 2 were more adequate than Scenario 1, with a significant improvement for all stations ranging from 4 % to 8 %.
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Cheng, Hanting, Wen Hu, Xiaohui Zhou, Rongshu Dong, Guodao Liu, Qinfen Li, and Xian Zhang. "Fruit Tree Legume Herb Intercropping Orchard System Is an Effective Method to Promote the Sustainability of Systems in a Karst Rocky Desertification Control Area." Forests 13, no. 10 (September 21, 2022): 1536. http://dx.doi.org/10.3390/f13101536.
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Karst rocky desertification control through the conversion of cropland to economic forest is vital for vegetation recovery and the alleviation of distinct contradiction between ecological conservation and economic development. To evaluate the sustainability of orchard systems from the perspectives of ecosystem and economic services, we employed emergy analysis for the comprehensive and quantitative assessment of two orchard system types: (1) mango monoculture (MM) and macadamia monoculture (NM) and (2) mango Vicia angustifolia intercropping (MVI) and macadamia Desmodium intortum intercropping (NDI). In the past, these areas were converted from a maize field (MF) in the southwest karst area of China. Our results showed that, compared to the MF, the total emergy input in monoculture orchards (NM and NM) decreased by 8.99% and 35.25%, and the economic profit (EP) increased by 20,406.57 and 114,406.32 RMB·ha−1, respectively. However, the non-renewable environmental input (energy loss of soil, SOM reduction, and irrigation water) still accounted for 43.25% and 62.01% in the total emergy input. After conversion to orchard legume herb intercropping (MVI and NDI), purchased resource inputs accounted for 86.36% and 68.20% of the total emergy input. Orchard legume herb intercropping further increased the EP, while improving ecosystem services and providing the capability for groundwater recharge, soil conservation, and soil carbon sequestration. The intercropping orchards were relatively sustainable from the view of economic and ecosystem services (EISD > 3.18), due to lower environmental loading ratios (ELR < 1.15), higher emergy yield ratio (EYR > 0.89), and economic output/input ratio (O/I ratio > 2.41). The integrated pest management simulations indicated that, compared to intercropping systems, the renewable percent (R%) and emergy sustainability index (ESI) of the scenario simulations (MVI-O and NDI-O) increased by 17.61% and 10.51%, respectively. These results suggest that integrated pest management is an effective method to improve the short-term sustainability of the orchard system. Therefore, the management of intercropped legume herb within an orchard system is an effective way to achieve sustainable development.
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Yang, Kang, Lei Qin, Zhongming Wang, Wei Feng, Pingzhong Feng, Shunni Zhu, Jingliang Xu, and Zhenhong Yuan. "Water-saving analysis on an effective water reuse system in biodiesel feedstock production based on Chlorella zofingiensis fed-batch cultivation." Water Science and Technology 71, no. 10 (March 27, 2015): 1562–68. http://dx.doi.org/10.2166/wst.2015.139.
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The micralgae-based biofuel obtained from dairy wastewater (DWW) is considered a promising source of energy. However, this process consumes water due to the concentration of wastewater being normally too high for some micoralgae cultivation, and dilution is always needed. In this work, the cultivation of microalgae has been examined in non-recirculated water (NR) and recirculated water systems (R). The growth of Chlorella zofingiensis and the nutrient removal of DWW have been recorded. The comparison indicates the R had a little more advantage in biomass and lipid output (1.55, 0.22 g, respectively) than the NR (1.51, 0.20 g, respectively). However, the total chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), and total phosphorus (TP) removals of the R were lower than those of the NR system during the culture. The highest removal of total COD, TKN, and TP were 85.05%, 93.64%, and 98.45%, respectively. Furthermore, no significant difference has been observed in the higher heating value and lipid content of the biomass of the R and NR. The results show the R can save 30% of the total water input during the culture. All above results indicate the R system has great potential in industry.
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Kou, Li, Hua, and Li. "Hydrochemical Characteristics, Controlling Factors, and Solute Sources of Streamflow and Groundwater in the Hei River Catchment, China." Water 11, no. 11 (November 1, 2019): 2293. http://dx.doi.org/10.3390/w11112293.
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Water scarcity in arid regions is exacerbated by water quality degradation from anthropogenic contamination. In water-scarce regions, it is crucial to identify hydrochemical characteristics and pollution sources for effective water resource management. In this study, the Hei River—located in the Loess Plateau of China, which is an arid region with substantial anthropogenic-induced environmental changes—was selected as the study area to investigate these issues. The major ions of 242 streamflow and groundwater samples were measured during the 2014 and 2015 dry and flood seasons. Using a Piper diagram, a fuzzy membership function, a Gibbs diagram, and a forward model, the hydrochemical facies and water quality of streamflow and groundwater were investigated, and the main river solute sources and relative contributions were determined using quantitative and qualitative methods. The total dissolved solids were 279.6 ± 127.8 mg·L−1 for streamflow and 354.0 ± 157.4 mg·L−1 for groundwater, indicating low salinity water. However, the hydrochemical characteristics varied with season and location. Qualitatively, the atmospheric inputs, human activities, and rock weathering all contributed solutes to the waters but with varying contributions. The following are the mean contributions of analyzed solute source: silicate weathering (45.1 ± 1.1%) > carbonate weathering (34.1 ± 1.6%) > evaporite dissolution (13.7 ± 2.4%) > atmospheric input (5.4 ± 0.1%) > anthropogenic input (1.7 ± 0.1%). In general, water quality was satisfactory, as the majority of samples conformed to drinking water standards. The samples had good water quality because the river solutes were not heavily affected by anthropogenic activities and were primarily controlled by rock weathering. However, localized areas of high anthropogenic impact were identified. Such locations should be prioritized for pollution control and water resource management.
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Schneider, Thomas. "Water movement in the firn of Storglaciären, Sweden." Journal of Glaciology 45, no. 150 (1999): 286–94. http://dx.doi.org/10.1017/s0022143000001787.
Full textAbstract:
AbstractThe hydraulic properties of the firn on Storglaciären, Sweden, were investigated in firn cores by water-table measurements and pumping tests. The mean density of the firn was 800 850 kg m3, giving an effective porosity of 0.073. The lower part of the firn layer was saturated with water, producing a maximum saturated layer of 5 m in late July. Hydraulic conductivity of the firn aquifer was determined from pumping tests to be 4.9 × 105m s1. Percolation velocity, calculated from the time lag of maximal water input at the glacier surface and the water-level peaks, was 0.25 m h1. Percolation velocity increased over the ablation season, indicating a widening of the percolation pathways. A decrease in percolation velocity with percolation depth was found, reflecting decreasing permeability. The firn–water table responded to water input at the glacier surface with a delay of about 3 days. No diurnal variations were found in an area which was not influenced by fast drainage, indicating a diffusion of diurnal variations in meltwater production. One borehole intersected a water-filled cavity. Water level in this cavity showed diurnal variations, which probably were caused by diurnally produced meltwater waves moving fast through englacial conduits.
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O'Geen, A. T., J. J. Maynard, and R. A. Dahlgren. "Efficacy of constructed wetlands to mitigate non-point source pollution from irrigation tailwaters in the San Joaquin Valley, California, USA." Water Science and Technology 55, no. 3 (February 1, 2007): 55–61. http://dx.doi.org/10.2166/wst.2007.072.
Full textAbstract:
The efficacy of using constructed wetlands (CWs) to sequester organic carbon and nutrients from irrigation tailwaters was studied in the San Joaquin Valley, California. Two CWs were monitored during the 2004 irrigation season, a new CW (W-1) and 10-year-old CW (W-2). Input/output waters from CW were collected weekly and analyzed for a variety of water quality contaminants. Organic carbon, nutrient and sediment retention efficiencies were evaluated from input/output concentrations. Characteristics of sediment were examined spatially at W-2. Results indicate that W-2 was more efficient at contaminant removal. Average particulate organic carbon retention, was 70±13% (mean±standard deviation) in W-2 and 48±32% in W-1. Chlorophyll-a, a measure of algal biomass, was higher at W-1, especially in input waters. Initially, output concentration of chlorophyll-a increased 15-fold in W-2, however over time, as emergent vegetation established, chlorophyll-a decreased to 35% of input levels. Average total N removal efficiency was 45±18% for W-2 compared to 22±32% in W-1. Total P removal efficiency was 72±14% at W-2 compared to 18±26% at W-1. CWs were most effective at removing total suspended solids, 84±15% and 97±2% for W-1 and W-2, respectively. Results demonstrate that CWs are effective at capturing POC, sediment and nutrients from irrigation tailwaters.
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Wang, Ke, Zhimin Zhou, and Yuehui Wang. "Flexible electrothermal polymer film based on reduced graphene oxide–water polyurethane." Modern Physics Letters B 34, no. 25 (June 6, 2020): 2050265. http://dx.doi.org/10.1142/s0217984920502656.
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In this paper, waterborne polyurethane (WPU) conductive films incorporated with reduced graphene oxide (RGO) as conductive fillers were prepared by solution blending and tape casting method. The electrical conductivity, thermal conductivity and microstructures of the composite films were systematically investigated. The experimental results demonstrate that the electrical conductivity and thermal conductivity of the RGO–WPU composite films first increased then decreased with the increase of the RGO content. The resistivity of composite film with 7% RGO reaches to the smallest that is about [Formula: see text], and the thermal conductivity of the composite film with 7% graphene was about 0.29 W.m.K[Formula: see text], which an increase of 70% compared with pure WPU. The electrical conductivity of the composite film decreased with the increase of the original concentration of WPU solution and thickness of the composite film. As film heater, the composite film displayed effective and rapid heating at low input voltages owing to the good conductivity. With an input voltage was in the range of 10–24 V, the film took less than 30s to reach a steady-state temperature, demonstrating the fast response of the composite film heater and suitable for applications in the field of the fast temperature switching with low input voltages as flexible electrothermal heater.
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Trabelsi, Fatma, and Salsebil Bel Hadj Ali. "Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia." Sustainability 14, no. 4 (February 18, 2022): 2341. http://dx.doi.org/10.3390/su14042341.
Full textAbstract:
Over the last years, the global application of machine learning (ML) models in groundwater quality studies has proved to be a robust alternative tool to produce highly accurate results at a low cost. This research aims to evaluate the ability of machine learning (ML) models to predict the quality of groundwater for irrigation purposes in the downstream Medjerda river basin (DMB) in Tunisia. The random forest (RF), support vector regression (SVR), artificial neural networks (ANN), and adaptive boosting (AdaBoost) models were tested to predict the irrigation quality water parameters (IWQ): total dissolved solids (TDS), potential salinity (PS), sodium adsorption ratio (SAR), exchangeable sodium percentage (ESP), and magnesium adsorption ratio (MAR) through low-cost, in situ physicochemical parameters (T, pH, EC) as input variables. In view of this, seventy-two (72) representative groundwater samples have been collected and analysed for major cations and anions during pre-and post-monsoon seasons of 3 years (2019–2021) to compute IWQ parameters. The performance of the ML models was evaluated according to Pearson’s correlation coefficient (r), the root means square error (RMSE), and the relative bias (RBIAS). The model sensitivity analysis was evaluated to identify input parameters that considerably impact the model predictions using the one-factor-at-time (OFAT) method of the Monte Carlo (MC) approach. The results show that the AdaBoost model is the most appropriate model for predicting all parameters (r was ranged between 0.88 and 0.89), while the random forest model is suitable for predicting only four parameters: TDS, PS, SAR, and ESP (r was with 0.65 to 0.87). Added to that, this study found out that the ANN and SVR models perform well in predicting three parameters (TDS, PS, SAR) and two parameters (PS, SAR), respectively, with the most optimal value of generalization ability (GA) close to unity (between 1 and 0.98). Moreover, the results of the uncertainty analysis confirmed the prominent superiority and robustness of the ML models to produce excellent predictions with only a few physicochemical parameters as inputs. The developed ML models are relevant for predicting cost-effective irrigation water quality indices and can be applied as a DSS tool to improve water management in the Medjerda basin.