Comparing the performance of support vector machine, gene expression programming and Bayesian networks in predicting river flow (Case study: Kashkan River)

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DEHGHANI R.; YOUNESI H.; Torabi Podeh H.

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Journal: Journal of Water and Soil Conservation Year:2017 | Volume:24 | Issue:4 Page(s): 161-177

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Title

Comparing the performance of support vector machine, gene expression programming and Bayesian networks in predicting river flow (Case study: Kashkan River)

Author(s)

DEHGHANI R. | YOUNESI H. | Torabi Podeh H. | Issue Writer Certificate

Keywords

Gene expression programmingQ1

ForecastQ2

Bayesian networkQ1

KashkanQ1

Support vector MachineQ1

Abstract

Background and Objectives: River flow prediction is one of the most important key issues in the management and planning of water resources, in particular the adoption of proper decisions in the event of floods and the occurrence of droughts. In order to predict the flow rate of rivers, various approaches have been introduced in hydrology, in which intelligent models are the most important ones. Materials and Methods: In this study, daily data from Kashkan watershed in Lorestan province was used to evaluate the accuracy of models in river flow prediction. Support vector Machine, Gene expression programming and Bayesian network were used to model the daily flow of Kashkan River and the results were compared with each other for the accuracy of the studied models. In a few studies, each of the models presented in the prediction of daily flow has been studied, but the purpose of this study is to simultaneously examine these models in a basin to predict the daily flow of the river. In this research, the Kashkan River in the Lorestan province was selected as the study area and the daily flow of observations of this basin was used at Pul-e-Dokhtar hydrometric station to calibrate and validate the models. For this purpose, at first 80% of the daily flow data (2004-2011) was selected for calibration of the models and 20% of the data (2012-2014) were used to validate the models. Gene expression programming is an automated scheduling method that provides problem solution using computer programming and is part of a family of evolutionary programming. A Support vector Machine is also an efficient learning system based on optimization theory. Also, the Basin network is a meaningful representation of our uncertain relationships between parameters in a process and a non-circular directional graph of nodes for displaying random variables and arcs to represent potential relationships between variables. The correlation coefficients, root mean square error, mean absolute error was used for evaluation and also comparison of the performance of models in this research. Results: The results showed that all three models have better results in structures of 1 to 5 daily times than other specified structures. In addition to, according to the evaluation criteria, it was found between the models used, the Support vector Machine model, the highest accuracy of R = 0. 910, the lowest root mean square error of RMSE=0. 002m3/s and the lowest absolute error value of MAE=0. 001m3/s at verification stage. Conclusions: The results showed that an increase in the number of effective parameters in different models for simulation results in better performance in the discharge estimation. In addition to, the results showed that the Support vector Machine model has a better performance than Gene expression programming and Bayesian networks.

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APA: Copy

DEHGHANI, R., YOUNESI, H., & Torabi Podeh, H.. (2017). Comparing the performance of support vector machine, gene expression programming and Bayesian networks in predicting river flow (Case study: Kashkan River). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 24(4 ), 161-177. SID. https://sid.ir/paper/156478/en

Vancouver: Copy

DEHGHANI R., YOUNESI H., Torabi Podeh H.. Comparing the performance of support vector machine, gene expression programming and Bayesian networks in predicting river flow (Case study: Kashkan River). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2017;24(4 ):161-177. Available from: https://sid.ir/paper/156478/en

IEEE: Copy

R. DEHGHANI, H. YOUNESI, and H. Torabi Podeh, “Comparing the performance of support vector machine, gene expression programming and Bayesian networks in predicting river flow (Case study: Kashkan River),” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 24, no. 4 , pp. 161–177, 2017, [Online]. Available: https://sid.ir/paper/156478/en

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