Journal articles on the topic 'Hydrology Linear programming'
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Elshorbagy, A., G. Corzo, S. Srinivasulu, and D. P. Solomatine. "Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology – Part 2: Application." Hydrology and Earth System Sciences Discussions 6, no. 6 (November 19, 2009): 7095–142. http://dx.doi.org/10.5194/hessd-6-7095-2009.
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Abstract. In this second part of the two-part paper, the data driven modeling (DDM) experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets) are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations) were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), Support vector machines (SVM), M5 model trees (M5), K nearest neighbors (K-nn), and multiple linear regression (MLR) techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike the two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it should not be ignored as a potential modeling technique for hydrological applications.
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Elshorbagy, A., G. Corzo, S. Srinivasulu, and D. P. Solomatine. "Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application." Hydrology and Earth System Sciences 14, no. 10 (October 14, 2010): 1943–61. http://dx.doi.org/10.5194/hess-14-1943-2010.
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Abstract. In this second part of the two-part paper, the data driven modeling (DDM) experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets) are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations) were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), Support vector machines (SVM), M5 model trees (M5), K-nearest neighbors (K-nn), and multiple linear regression (MLR) techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it should not be ignored as a potential modeling technique for hydrological applications.
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Wawrzosek, Jacek, and Szymon Ignaciuk. "Postoptimization of the model of water supply for urban and industrial agglomeration." ITM Web of Conferences 23 (2018): 00035. http://dx.doi.org/10.1051/itmconf/20182300035.
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A case study of the tools used by an analyst of the economic aspects of the operation of the water supply network has been undertaken in this paper. All issues discussed here are formulated by using degenerated linear programming models ( PL ). Below, it is noted that the linear dependence of binding constraints ( CO ) distorts standard postoptimization procedures in PL. This observed fact makes postoptimization analysis mostly unhelpful for an average analyst due to problems with the int erpretation of ambiguous sensitivity reports which are obtained from popular computer packages. In standard postoptimization methods, changes to single parameters of the right-hand vector CO are analyzed or referred to parametric linear programming that unfortunately requires prior knowledge of mathematically and economically justified vectors of changes of right-hand sides CO. Therefore, it is suggested that modifications are introduced to some of the postoptimization procedures in this work. For issues in the field of hydrology, the following were presented: interpretation and methods of generating justified vectors of changes of right-hand sides of limiting conditions. And so, the procedure of generating infinitely many solutions of the dual issue based on certain vectors orthogonal to the vector of right-hand sides of constraint conditions was demonstrated. Furthermore, the same orthogonal vectors were used to obtain nodal solutions of the dua0l model and the corresponding vectors of changes of the entire right-hand sides of the constraint conditions. Then, managerial interpretation was applied to this way of proceeding. The methods presented in the work serve to improve the functioning of the system of water supply.
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Niazkar, Majid, and Mohammad Zakwan. "Application of MGGP, ANN, MHBMO, GRG, and Linear Regression for Developing Daily Sediment Rating Curves." Mathematical Problems in Engineering 2021 (December 23, 2021): 1–13. http://dx.doi.org/10.1155/2021/8574063.
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A data-driven relationship between sediment and discharge of a river is among the most erratic relationships in river engineering due to the existence of an inevitable scatter in sediment rating curves. Recently, Multigene Genetic Programming (MGGP), as a machine learning (ML) method, has been proposed to develop data-driven models for various phenomena in the field of hydrology and water resource engineering. The present study explores the capability of MGGP-based models to develop daily sediment ratings of two gauging sites with 30-year sediment-discharge data, which was utilized previously in the literature. The results obtained by MGGP were compared with those achieved by an empirical model and Artificial Neural Network (ANN). The coefficients of the empirical model were calibrated using linear and nonlinear regression models (Generalized Reduced Gradient (GRG) and the Modified Honey Bee Mating Optimization (MHBMO) algorithm). According to the comparative analysis, the mean absolute error (MAE) at the two gauging stations reduced from 516.54 to 519.23 obtained by nonlinear regression to 447.26 and 504.23 achieved by MGGP, respectively. Similarly, all other performance indices indicated the suitability and accuracy of MGGP in developing sediment ratings. Therefore, it was demonstrated that ML-based models, particularly MGGP-based models, outperformed the empirical models for estimating sediment loads.
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Huang, Mutao, and Yong Tian. "A novel visual modeling system for time series forecast: application to the domain of hydrology." Journal of Hydroinformatics 15, no. 1 (July 20, 2012): 21–37. http://dx.doi.org/10.2166/hydro.2012.158.
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Accurate and reliable forecasts of key hydrological variables such as stream flow are of importance due to their profound impacts on real world water resources applications. Data-driven methods have proven their applicability to modeling complex and non-linear hydrological processes. This paper presents a novel visual modeling system that has been developed to overcome the problems involved in implementation of data-driven models for hydrological forecasts using conventional programming languages: problems such as the effort and skill needed to program the models, the lack of reusability of existing models, and the lack of shared tools to perform tedious tasks such as preprocessing data. The system provides an integrated visual modeling environment within which users are able to graphically design and verify specific forecasting models for particular problems without writing code. A set of popular data-driven models are offered by the system. Plug-in models created by wrapping existing code are also allowed to run within the system due to the system's open architecture. The system's feasibility and capability is demonstrated through a case study of forecasting 1-day ahead flow in a river basin located in China. The encouraging simulation results show that the system can simplify the process of implementing hydrological forecast.
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Elshorbagy, A., G. Corzo, S. Srinivasulu, and D. P. Solomatine. "Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology – Part 1: Concepts and methodology." Hydrology and Earth System Sciences Discussions 6, no. 6 (November 19, 2009): 7055–93. http://dx.doi.org/10.5194/hessd-6-7055-2009.
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Abstract. A comprehensive data driven modeling experiment is presented in two-part paper. In this first part, an extensive data-driven modeling experiment is proposed. The most important concerns regarding the way data driven modeling (DDM) techniques and data were handled, compared, and evaluated, and the basis on which findings and conclusions were drawn are discussed. A concise review of key articles that presented comparisons among various DDM techniques is presented. Six DDM techniques, namely, neural networks, genetic programming, evolutionary polynomial regression, support vector machines, M5 model trees, and K-nearest neighbors are proposed and explained. Multiple linear regression and naïve models are also suggested as baseline for comparison with the various techniques. Five datasets from Canada and Europe representing evapotranspiration, upper and lower layer soil moisture content, and rainfall-runoff process are described and proposed for the modeling experiment. Twelve different realizations (groups) from each dataset are created by a procedure involving random sampling. Each group contains three subsets; training, cross-validation, and testing. Each modeling technique is proposed to be applied to each of the 12 groups of each dataset. This way, both predictive accuracy and uncertainty of the modeling techniques can be evaluated. The implementation of the modeling techniques, results and analysis, and the findings of the modeling experiment are deferred to the second part of this paper.
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Elshorbagy, A., G. Corzo, S. Srinivasulu, and D. P. Solomatine. "Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology." Hydrology and Earth System Sciences 14, no. 10 (October 14, 2010): 1931–41. http://dx.doi.org/10.5194/hess-14-1931-2010.
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Abstract. A comprehensive data driven modeling experiment is presented in a two-part paper. In this first part, an extensive data-driven modeling experiment is proposed. The most important concerns regarding the way data driven modeling (DDM) techniques and data were handled, compared, and evaluated, and the basis on which findings and conclusions were drawn are discussed. A concise review of key articles that presented comparisons among various DDM techniques is presented. Six DDM techniques, namely, neural networks, genetic programming, evolutionary polynomial regression, support vector machines, M5 model trees, and K-nearest neighbors are proposed and explained. Multiple linear regression and naïve models are also suggested as baseline for comparison with the various techniques. Five datasets from Canada and Europe representing evapotranspiration, upper and lower layer soil moisture content, and rainfall-runoff process are described and proposed, in the second paper, for the modeling experiment. Twelve different realizations (groups) from each dataset are created by a procedure involving random sampling. Each group contains three subsets; training, cross-validation, and testing. Each modeling technique is proposed to be applied to each of the 12 groups of each dataset. This way, both prediction accuracy and uncertainty of the modeling techniques can be evaluated. The description of the datasets, the implementation of the modeling techniques, results and analysis, and the findings of the modeling experiment are deferred to the second part of this paper.
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Shen, Jianjian, Xiufei Zhang, Jian Wang, Rui Cao, Sen Wang, and Jun Zhang. "Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions." Energies 12, no. 1 (January 2, 2019): 144. http://dx.doi.org/10.3390/en12010144.
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This paper focuses on the monthly operations of an interprovincial hydropower system (IHS) connected by ultrahigh voltage direct current lines. The IHS consists of the Xiluodu Hydropower Project, which ranks second in China, and local plants in multiple recipient regions. It simultaneously provides electricity for Zhejiang and Guangdong provinces and thus meets their complex operation requirements. This paper develops a multi-objective optimization model of maximizing the minimum of total hydropower generation for each provincial power grid while considering network security constraints, electricity contracts, and plant constraints. The purpose is to enhance the minimum power in dry season by using the differences in hydrology and regulating storage of multiple rivers. The TOPSIS method is utilized to handle this multi-objective optimization, where the complex minimax objective function is transformed into a group of easily solved linear formulations. Nonlinearities of the hydropower system are approximatively described as polynomial formulations. The model was used to solve the problem using mixed integer nonlinear programming that is based on the branch-and-bound technique. The proposed method was applied to the monthly generation scheduling of the IHS. Compared to the conventional method, both the total electricity for Guangdong Power Grid and Zhejiang Power Grid during dry season increased by 6% and 4%, respectively. The minimum monthly power also showed a significant increase of 40% and 31%. It was demonstrated that the hydrological differences between Xiluodu Plant and local hydropower plants in receiving power grids can be fully used to improve monthly hydropower generation.
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Babovic, Vladan, and Maarten Keijzer. "Rainfall Runoff Modelling Based on Genetic Programming." Hydrology Research 33, no. 5 (October 1, 2002): 331–46. http://dx.doi.org/10.2166/nh.2002.0012.
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The runoff formation process is believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases it is difficult to collect all the data necessary for such a model. By using data driven models such as genetic programming (GP), one can attempt to model runoff on the basis of available hydrometeorological data. This work addresses use of genetic programming for creating rainfall-runoff models on the basis of data alone, as well as in combination with conceptual models (i.e taking advantage of knowledge about the problem domain).
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Gillig, Dhazn, Bruce A. McCarl, and Frederick Boadu. "An Economic, Hydrologic, and Environmental Assessment of Water Management Alternative Plans for the South Central Texas Region." Journal of Agricultural and Applied Economics 33, no. 1 (April 2001): 59–78. http://dx.doi.org/10.1017/s1074070800020782.
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AbstractRegional water scarcity has motivated the South Central Texas Regional Water Planning Group to actively develop water management plans to address long-/short-term regional water needs. This study, therefore, develops an integrated Edwards Aquifer groundwater and river system simulation model to determine the “best” choice of regional water management plans using mixed-integer linear programming. The economic, hydrologic, and environmental consequences of the “best” choice of regional and other water management plans and options are evaluated and compared. Results indicate a tradeoff between the economic and environmental benefits. A slight decrease in economic benefit results in a substantial increase in environmental benefit.
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Nourani, Vahid, Mehdi Komasi, and Mohamad Taghi Alami. "Geomorphology-based genetic programming approach for rainfall–runoff modeling." Journal of Hydroinformatics 15, no. 2 (October 10, 2012): 427–45. http://dx.doi.org/10.2166/hydro.2012.113.
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Nowadays, artificial intelligence approaches such as artificial neural network (ANN) as a self-learn non-linear simulator and genetic programming (GP) as a tool for function approximations are widely used for rainfall–runoff modeling. Both approaches are usually created based on temporal characteristics of the process. Hence, the motivation to present a comprehensive model which also employs the watershed geomorphological features as spatial data. In this paper, two different scenarios, separated and integrated geomorphological GP (GGP) modeling based on observed time series and spatially varying geomorphological parameters, were presented for rainfall–runoff modeling of the Eel River watershed. In the first scenario, the model could present a good insight into the watershed hydrologic operation via GGP formulation. In the second scenario, an integrated model was proposed to predict runoff in stations with lack of data or any point within the watershed due to employing the spatially variable geomorphic parameters and rainfall time series of the sub-basins as the inputs. This ability of the integrated model for the spatiotemporal modeling of the process was examined through the cross-validation technique. The results of this research demonstrate the efficiency of the proposed approaches due to taking advantage of geomorphological features of the watershed.
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Antón, J. M., J. B. Grau, J. M. Cisneros, F. V. Laguna, P. L. Aguado, J. J. Cantero, D. Andina, and E. Sánchez. "Continuous multi-criteria methods for crop and soil conservation planning on La Colacha (Río Cuarto, Province of Córdoba, Argentina)." Natural Hazards and Earth System Sciences 12, no. 8 (August 13, 2012): 2529–43. http://dx.doi.org/10.5194/nhess-12-2529-2012.
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Abstract. Agro-areas of Arroyos Menores (La Colacha) west and south of Río Cuarto (Prov. of Córdoba, Argentina) basins are very fertile but have high soil loses. Extreme rain events, inundations and other severe erosions forming gullies demand urgently actions in this area to avoid soil degradation and erosion supporting good levels of agro production. The authors first improved hydrologic data on La Colacha, evaluated the systems of soil uses and actions that could be recommended considering the relevant aspects of the study area and applied decision support systems (DSS) with mathematic tools for planning of defences and uses of soils in these areas. These were conducted here using multi-criteria models, in multi-criteria decision making (MCDM); first of discrete MCDM to chose among global types of use of soils, and then of continuous MCDM to evaluate and optimize combined actions, including repartition of soil use and the necessary levels of works for soil conservation and for hydraulic management to conserve against erosion these basins. Relatively global solutions for La Colacha area have been defined and were optimised by Linear Programming in Goal Programming forms that are presented as Weighted or Lexicographic Goal Programming and as Compromise Programming. The decision methods used are described, indicating algorithms used, and examples for some representative scenarios on La Colacha area are given.
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Dalin, Carole, Huanguang Qiu, Naota Hanasaki, Denise L. Mauzerall, and Ignacio Rodriguez-Iturbe. "Balancing water resource conservation and food security in China." Proceedings of the National Academy of Sciences 112, no. 15 (March 30, 2015): 4588–93. http://dx.doi.org/10.1073/pnas.1504345112.
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China’s economic growth is expected to continue into the next decades, accompanied by sustained urbanization and industrialization. The associated increase in demand for land, water resources, and rich foods will deepen the challenge of sustainably feeding the population and balancing agricultural and environmental policies. We combine a hydrologic model with an economic model to project China’s future food trade patterns and embedded water resources by 2030 and to analyze the effects of targeted irrigation reductions on this system, notably on national agricultural water consumption and food self-sufficiency. We simulate interprovincial and international food trade with a general equilibrium welfare model and a linear programming optimization, and we obtain province-level estimates of commodities’ virtual water content with a hydrologic model. We find that reducing irrigated land in regions highly dependent on scarce river flow and nonrenewable groundwater resources, such as Inner Mongolia and the greater Beijing area, can improve the efficiency of agriculture and trade regarding water resources. It can also avoid significant consumption of irrigation water across China (up to 14.8 km3/y, reduction by 14%), while incurring relatively small decreases in national food self-sufficiency (e.g., by 3% for wheat). Other researchers found that a national, rather than local, water policy would have similar effects on food production but would only reduce irrigation water consumption by 5%.
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Madhuri, R., Y. S. L. Sarath Raja, and K. Srinivasa Raju. "Simulation-optimization framework in urban flood management for historic and climate change scenarios." Journal of Water and Climate Change 13, no. 2 (December 21, 2021): 1007–24. http://dx.doi.org/10.2166/wcc.2021.436.
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Abstract A simulation-optimization framework is established by integrating Hydrologic Engineering Center Hydraulic Modeling System (HEC-HMS) for computation of runoff, siting tool EPA System for Urban Storm-water Treatment and Analysis INtegration (EPA-SUSTAIN) for placement of Best Management Practices (BMPs), and Binary Linear Integer Programming (BLIP) for runoff reduction. The framework is applied to an urban catchment, namely Greater Hyderabad Municipal Corporation (GHMC). The rainfall-runoff analysis was conducted for extreme rainfalls for historic (2016) and future events in 2050 and 2064 under Representative Concentration Pathways (RCPs) 6.0 and 8.5. The simulation-optimization approach in the historic scenario yielded 495,607 BMPs occupying 76.99 km2 resulting in runoff reduction of 21.54 mm (198.76–177.22 mm). Achieved runoff reduction is 38.72 (428.35–389.63 mm) and 55.03 (602.65–547.62 mm), respectively, for RCPs 6.0 and 8.5, which could meet the water demands of GHMC for 10.33 and 11.53 days. Impacts of 10 different BMP configurations of varying costs (10–70%) and pollutant load reductions (0–3%) on runoff reduction are accomplished as part of sensitivity analysis.
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Zakwan, Mohammad, and Majid Niazkar. "A Comparative Analysis of Data-Driven Empirical and Artificial Intelligence Models for Estimating Infiltration Rates." Complexity 2021 (May 4, 2021): 1–13. http://dx.doi.org/10.1155/2021/9945218.
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Infiltration is a vital phenomenon in the water cycle, and consequently, estimation of infiltration rate is important for many hydrologic studies. In the present paper, different data-driven models including Multiple Linear Regression (MLR), Generalized Reduced Gradient (GRG), two Artificial Intelligence (AI) techniques (Artificial Neural Network (ANN) and Multigene Genetic Programming (MGGP)), and the hybrid MGGP-GRG have been applied to estimate the infiltration rates. The estimated infiltration rates were compared with those obtained by empirical infiltration models (Horton’s model, Philip’s model, and modified Kostiakov’s model) for the published infiltration data. Among the conventional models considered, Philip’s model provided the best estimates of infiltration rate. It was observed that the application of the hybrid MGGP-GRG model and MGGP improved the estimates of infiltration rates as compared to conventional infiltration model, while ANN provided the best prediction of infiltration rates. To be more specific, the application of ANN and the hybrid MGGP-GRG reduced the sum of square of errors by 97.86% and 81.53%, respectively. Finally, based on the comparative analysis, implementation of AI-based models, as a more accurate alternative, is suggested for estimating infiltration rates in hydrological models.
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Ilich, Nesa, Slobodan P. Simonovic, and Mochtar Amron. "The benefits of computerized real-time river basin management in the Malahayu reservoir system." Canadian Journal of Civil Engineering 27, no. 1 (February 15, 2000): 55–64. http://dx.doi.org/10.1139/l99-051.
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This paper describes the developments of operating rules for the Malahayu reservoir system in Indonesia. The analysis in this study is based on the use of a simulation model with a nested network flow optimization subprogram, which required hydrologic time series of reservoir inflows as input data. Since estimates of historic naturalized flows were of insufficient length, they were used as a basis for developing a 100-year stochastic series which offered a more challenging input while preserving the relevant statistics of the original historic series. This study shows by how much the reservoir yield could have been increased in the past, assuming that short-term inflow and demand forecasts are available and that the proposed reservoir operating rule is obeyed. The increase is estimated by comparing the long- term average of the simulated diversions at the three weirs with the actual historic diversions which are on the record. A more efficient reservoir operating policy would increase the reservoir yield by 38% in the March-June period and by 33% in the July-October period. If additional local runoff into the weirs is utilized in the same period, the increased supply would range up to 66% in the March-June period and 43% in the July-October period. The results from this study provide a strong argument in favour of investing in modern technology as opposed to massive additional infrastructure development. Key words: linear programming, reservoir rule curve, simulation, optimization, stochastic inflow series.
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Jha, Madan K., Richard C. Peralta, and Sasmita Sahoo. "Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India." International Journal of Environmental Research and Public Health 17, no. 10 (May 18, 2020): 3521. http://dx.doi.org/10.3390/ijerph17103521.
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Water resources sustainability is a worldwide concern because of climate variability, growing population, and excessive groundwater exploitation in order to meet freshwater demand. Addressing these conflicting challenges sometimes can be aided by using both simulation and mathematical optimization tools. This study combines a groundwater-flow simulation model and two optimization models to develop optimal reconnaissance-level water management strategies. For a given set of hydrologic and management constraints, both of the optimization models are applied to part of the Mahanadi River basin groundwater system, which is an important source of water supply in Odisha State, India. The first optimization model employs a calibrated groundwater simulation model (MODFLOW-2005, the U.S. Geological Survey modular ground-water model) within the Simulation-Optimization MOdeling System (SOMOS) module number 1 (SOMO1) to estimate maximum permissible groundwater extraction, subject to suitable constraints that protect the aquifer from seawater intrusion. The second optimization model uses linear programming optimization to: (a) optimize conjunctive allocation of surface water and groundwater and (b) to determine a cropping pattern that maximizes net annual returns from crop yields, without causing seawater intrusion. Together, the optimization models consider the weather seasons, and the suitability and variability of existing cultivable land, crops, and the hydrogeologic system better than the models that do not employ the distributed maximum groundwater pumping rates that will not induce seawater intrusion. The optimization outcomes suggest that minimizing agricultural rice cultivation (especially during the non-monsoon season) and increasing crop diversification would improve farmers’ livelihoods and aid sustainable use of water resources.
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Zhang, Yunquan, and Peiling Yang. "Agricultural Water Optimal Allocation Using Minimum Cross-Entropy and Entropy-Weight-Based TOPSIS Method in Hetao Irrigation District, Northwest China." Agriculture 12, no. 6 (June 13, 2022): 853. http://dx.doi.org/10.3390/agriculture12060853.
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Affected by the temporal and spatial changes of natural resources, human activities, and social economic system policies, there are many uncertainties in the development, utilization, and management process of irrigation district agricultural water resources, which will increase the complexity of the use of irrigation district agricultural water resources. Decision makers find it challenging to cope with the complexity of fluctuating water supplies and demands that are critical for water resources’ allocation. In response to these issues, this paper presents an optimization modeling approach for agricultural water allocation at an irrigation district scale, considering the uncertainties of water supply and demand. The minimum cross-entropy method was used to estimate the parameters of hydrologic frequency distribution functions of water supply and demand, which are the basis for agricultural water resources’ optimal allocation and the evaluation of water resources’ carrying capacity in the Hetao Irrigation District. Interval Linear Fractional Programming was used to find water availability, shortage, and use efficiency in different irrigation areas of the Hetao Irrigation District (HID) under different scenarios. The denominator of fractional planning is the environmental goal, and the numerator is the economic goal; so, the objective function of fractional programming is the utility rate required in the post-optimization analysis. Future water availability and shortage scenarios are adopted consistent with the Representative Concentration Pathways’ (RCPs’) framework, and future water use scenarios are developed using the Shared Socioeconomic Pathways’ (SSPs’) framework. Results revealed that under SSP1, the annual water consumption increased from 30 billion m3 to 60 billion m3, almost doubling in Urad. The annual water consumption under SSP2 and SSP3 increased slightly, from 30 billion m3 to about 50 billion m3. The amount of water available for well irrigation in Urad decreased from 300 to 250 billion m3, while the amount of water available for canal irrigation in Urad remained at 270 billion m3 from 2010 s to 2030 s, only dropping to 240 billion m3 in 2040 s. The entropy-weight-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method was applied to evaluate agricultural water resources’ allocation schemes because it can avoid the subjectivity of weight determination and can reflect the dynamic changing trend of irrigation district agricultural water resources’ carrying capacity. The approach is applicable to most regions, such as the Hetao Irrigation District in the Upper Yellow River Basi with limited precipitation, to determine water strategies under the changing environment.
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Kaur, Ravinder, Rajesh Srivastava, Rajeev Betne, Kamal Mishra, and D. Dutta. "Integration of linear programming and a watershed-scale hydrologic model for proposing an optimized land-use plan and assessing its impact on soil conservation—A case study of the Nagwan watershed in the Hazaribagh district of Jharkhand, India." International Journal of Geographical Information Science 18, no. 1 (January 2004): 73–98. http://dx.doi.org/10.1080/13658810310001620915.
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