Reservoir Water Level Prediction Using Supervised Intelligent Committee Machine Method, Case Study: Karaj Amirkabir Dam

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MOHAMMAD REZAPOUR TABARI M.; Malekpour Shahraki M.M.

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Journal: IRAN-WATER RESOURCES RESEARCH Year:2019 | Volume:14 | Issue:5 Page(s): 14-26

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Title

Reservoir Water Level Prediction Using Supervised Intelligent Committee Machine Method, Case Study: Karaj Amirkabir Dam

Author(s)

MOHAMMAD REZAPOUR TABARI M. | Malekpour Shahraki M.M. | Issue Writer Certificate

Keywords

PredictionQ2

Reservoir Water LevelQ1

Karaj Amirkabir DamQ1

Supervised Intelligent Committee MachineQ1

Soft ModelsQ1

Abstract

The proper Prediction of water level variation in dam reservoirs is considered as one of the important issues for design and operation of dams and water supply management. In this study, based on five Soft Models such as support vector regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), artificial neural network (ANN), radial basis function neural network (RBFNN), and generalized regression neural network (GRNN) and the combined use of their results as input to one of these five models, a new structure called Supervised Intelligent Committee Machine (SICM) was proposed to predict the monthly Reservoir Water Level of Karaj Amirkabir Dam. The data used in this paper are water level, precipitation, evaporation, and inflow to and outflow from the dam. The evaluation of these models was done by nine error indexes and also the best model among all was selected using Vikor decision maker method. Evaluations showed that among the used Soft Models, the ANN was the best model with Nash– Sutcliffe efficiency (NS) and mean square error (MSE) equal to 0. 89 and 23. 37 square meters, respectively. The results of the proposed approach showed that the supervised (hybrid) neural network (SICM-ANN) has been able to provide high performance in predicting the monthly Reservoir Water Level in Karaj dam with increasing the NS coefficient to 0. 94 and decreasing the MSE index to 12. 85 square meters (more than 45 percent decrease). Accordingly, hybrid use of Soft Models can effectively be applied for a significant reduction in the predicted error of water level compared to single models.

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APA: Copy

MOHAMMAD REZAPOUR TABARI, M., & Malekpour Shahraki, M.M.. (2019). Reservoir Water Level Prediction Using Supervised Intelligent Committee Machine Method, Case Study: Karaj Amirkabir Dam. IRAN-WATER RESOURCES RESEARCH, 14(5 ), 14-26. SID. https://sid.ir/paper/100188/en

Vancouver: Copy

MOHAMMAD REZAPOUR TABARI M., Malekpour Shahraki M.M.. Reservoir Water Level Prediction Using Supervised Intelligent Committee Machine Method, Case Study: Karaj Amirkabir Dam. IRAN-WATER RESOURCES RESEARCH[Internet]. 2019;14(5 ):14-26. Available from: https://sid.ir/paper/100188/en

IEEE: Copy

M. MOHAMMAD REZAPOUR TABARI, and M.M. Malekpour Shahraki, “Reservoir Water Level Prediction Using Supervised Intelligent Committee Machine Method, Case Study: Karaj Amirkabir Dam,” IRAN-WATER RESOURCES RESEARCH, vol. 14, no. 5 , pp. 14–26, 2019, [Online]. Available: https://sid.ir/paper/100188/en

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