Continuous Rainfall-Runoff Simulation by Artificial Neural Networks Based on Selection of Effective Input Variables Using Partial Mutual Information (PMI) Algorithm

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Shafeizadeh M.; FATHIAN H.; Nikbakht Shahbazi a.r.

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Journal: IRAN-WATER RESOURCES RESEARCH Year:2019 | Volume:15 | Issue:2 Page(s): 144-161

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Title

Continuous Rainfall-Runoff Simulation by Artificial Neural Networks Based on Selection of Effective Input Variables Using Partial Mutual Information (PMI) Algorithm

Author(s)

Shafeizadeh M. | FATHIAN H. | Nikbakht Shahbazi a.r. | Issue Writer Certificate

Keywords

Artificial Neural NetworksQ2

PMI AlgorithmQ1

Continuous Rainfall-Runoff SimulationQ1

Abstract

Knowledge on the natural potential of basins is one of fundamental needs to optimal utilization of runoff and thus, rainfall-runoff simulation in basins is of utmost importance. Continuous simulation of rainfall-runoff in Maroun basin is performed in this study using Artificial Neural Networks (ANNs) in order to evaluate the ability and accuracy of ANN for runoff estimation. Considering the fact that the number of rainy days per year is less than sunny days, so runoff is caused by two different mechanisms. In continuous rainfall time and a few days later, runoff mainly is from high discharge and low base time. But on most days, when there is no rainfall, low baseflow and long base time form the outflow. Thus, in this research a dual criteria model of rainfall-runoff including model on rainy days and non-rainy days were examined. Also the input variables effective on runoff in the Maroun basin are determined using the partial mutual information (PMI) algorithm. Comparison of statistical criteria between the single criterion model and dual criteria model indicated that the latter was more accurate. Therefore, the Nash-Sutcliff coefficient of single criterion model and dual criteria model for test stage of network were 0. 86 and 0. 94 respectively.

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APA: Copy

Shafeizadeh, M., FATHIAN, H., & Nikbakht Shahbazi, a.r.. (2019). Continuous Rainfall-Runoff Simulation by Artificial Neural Networks Based on Selection of Effective Input Variables Using Partial Mutual Information (PMI) Algorithm. IRAN-WATER RESOURCES RESEARCH, 15(2 ), 144-161. SID. https://sid.ir/paper/100225/en

Vancouver: Copy

Shafeizadeh M., FATHIAN H., Nikbakht Shahbazi a.r.. Continuous Rainfall-Runoff Simulation by Artificial Neural Networks Based on Selection of Effective Input Variables Using Partial Mutual Information (PMI) Algorithm. IRAN-WATER RESOURCES RESEARCH[Internet]. 2019;15(2 ):144-161. Available from: https://sid.ir/paper/100225/en

IEEE: Copy

M. Shafeizadeh, H. FATHIAN, and a.r. Nikbakht Shahbazi, “Continuous Rainfall-Runoff Simulation by Artificial Neural Networks Based on Selection of Effective Input Variables Using Partial Mutual Information (PMI) Algorithm,” IRAN-WATER RESOURCES RESEARCH, vol. 15, no. 2 , pp. 144–161, 2019, [Online]. Available: https://sid.ir/paper/100225/en

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