RIVER FLOOD ROUTING USING EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS

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FATHIAN H.; NIKOO M.; NIKOO M.

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Journal: Journal of Water Resources Engineering Year:2011 | Volume:3 | Issue:5-6-7 Page(s): 13-23

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Information Journal Paper

Title

RIVER FLOOD ROUTING USING EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS

Author(s)

FATHIAN H. | NIKOO M. | NIKOO M. | Issue Writer Certificate

Keywords

FLOOD ROUTINGQ2

EVOLUTIONARY ARTIFICIAL NEURAL NETWORKQ3

TIME SERIESQ2

GENETIC ALGORITHMQ2

Abstract

FLOOD ROUTING is one of the methods for flood forecasting in rivers to control and manage flood in it. Today, new techniques using EVOLUTIONARY ARTIFICIAL NEURAL NETWORKs (EANN) based on Artificial Intelligence (AI) is used widely in various fields, especially in water engineering. In this research, routed flood in Karun river, reach Ahwaz-Farsiat using Feed Forward (FF), Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) of artificial neural networks. The linear cross correlation method was used for efficient input variables selection. The Algorithm Genetic method was used for determining optimum number of nodes in hidden layer of EANNs. Results show that can select efficient input variables by using cross correlation method. In addition the output of models compared with actual values shows that selected MLP model rather to Muskingum and MIKE11 have ability, flexibility and greater accuracy in forecasting and FLOOD ROUTING in Karun river.

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APA: Copy

FATHIAN, H., NIKOO, M., & NIKOO, M.. (2011). RIVER FLOOD ROUTING USING EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS. WATER ENGINEERING, 3(5-6-7), 13-23. SID. https://sid.ir/paper/169496/en

Vancouver: Copy

FATHIAN H., NIKOO M., NIKOO M.. RIVER FLOOD ROUTING USING EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS. WATER ENGINEERING[Internet]. 2011;3(5-6-7):13-23. Available from: https://sid.ir/paper/169496/en

IEEE: Copy

H. FATHIAN, M. NIKOO, and M. NIKOO, “RIVER FLOOD ROUTING USING EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS,” WATER ENGINEERING, vol. 3, no. 5-6-7, pp. 13–23, 2011, [Online]. Available: https://sid.ir/paper/169496/en

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