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Статті в журналах з теми "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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Дисертації з теми "Hydrology Linear programming"
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Bliss, Michael A. "Procedures to Perform Dam Rehabilitation Analysis in Aging Dams." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/33157.
Повний текст джерелаАнотація:
There are hundreds of existing dams within the State of Virginia, and even thousands more specifically within the United States. A large portion of these dams do not meet the current safety standard of passing the Probable Maximum Flood. Likewise, many of the dams have reached or surpassed the original design lives, and are in need of rehabilitation. A standard protocol will assist dam owners in completing a dam rehabilitation analysis. The protocol provides the methods to complete the hydrologic, hydraulic, and economic analysis. Additionally, alternative augmentation techniques are discussed including the integration of GIS applications and linear programming optimization techniques. The standard protocol and alternative techniques are applied to a case study. The case study includes a set of flood control dams located in the headwaters of the South River watershed in Augusta County, VA. The downstream impacts of the flood control dams on the city of Waynesboro are demonstrated through the hydrologic and hydraulic analysis.Master of Science
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"Optimization Model for Design of Vegetative Filter Strips for Stormwater Management and Sediment Control." Master's thesis, 2015. http://hdl.handle.net/2286/R.I.35989.
Повний текст джерелаАнотація:
abstract: Vegetative filter strips (VFS) are an effective methodology used for storm water management particularly for large urban parking lots. An optimization model for the design of vegetative filter strips that minimizes the amount of land required for stormwater management using the VFS is developed in this study. The resulting optimization model is based upon the kinematic wave equation for overland sheet flow along with equations defining the cumulative infiltration and infiltration rate.
In addition to the stormwater management function, Vegetative filter strips (VFS) are effective mechanisms for control of sediment flow and soil erosion from agricultural and urban lands. Erosion is a major problem associated with areas subjected to high runoffs or steep slopes across the globe. In order to effect economy in the design of grass filter strips as a mechanism for sediment control & stormwater management, an optimization model is required that minimizes the land requirements for the VFS. The optimization model presented in this study includes an intricate system of equations including the equations defining the sheet flow on the paved and grassed area combined with the equations defining the sediment transport over the vegetative filter strip using a non-linear programming optimization model. In this study, the optimization model has been applied using a sensitivity analysis of parameters such as different soil types, rainfall characteristics etc., performed to validate the modelDissertation/Thesis
Masters Thesis Civil and Environmental Engineering 2015
Частини книг з теми "Hydrology Linear programming"
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Yakowitz, Diana S. "A Regularized Algorithm for Solving Two-Stage Stochastic Linear Programming Problems: A Water Resources Example." In Stochastic and Statistical Methods in Hydrology and Environmental Engineering, 271–84. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1072-3_21.
Повний текст джерела