Simulation of (ANFIS) rainfall-runoff process using geomorphology based adaptive neuro- fuzzy inference system

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GHOLAMI SHABANALI; VAFAKHAH MEHDI; Ghaderi Kamal; JAVADI MOHAMMAD REZA

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Journal: CASPIAN JOURNAL OF ENVIRONMENTAL SCIENCES (CJES) Year:2020 | Volume:18 | Issue:2 Page(s): 109-122

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Title

Simulation of (ANFIS) rainfall-runoff process using geomorphology based adaptive neuro- fuzzy inference system

Author(s)

GHOLAMI SHABANALI | VAFAKHAH MEHDI | Ghaderi Kamal | JAVADI MOHAMMAD REZA | Issue Writer Certificate

Keywords

Physical characteristics of watershedQ3

Rainfall-runoff modelingQ3

Black box modelingQ3

Artificial intelligenceQ3

Geomorphologic unit hydrographQ3

Abstract

This research was conducted to present an integrated rainfall-runoff model based on the physical characteristics of the watershed, and to predict discharge not only in the outlet, but also at any desired point within the basin. To achieve this goal, a matrix of hydro-climatic variables (i. e. daily rainfall and daily discharge) and geomorphologic characteristics such as upstream drainage area (A), mean slope of watershed (S) and curve number (CN) was designed and simulated using Artificial intelligence techniques. Integrated Geomorphology-based Artificial Neural Network (IGANN) model with Root Mean Squared Error (RMSE) of 0. 02786 m3 s-1 and Nash-Sutcliffe Efficiency (NSE) of 0. 9403 and Integrated Geomorphology-based Adaptive Neuro-Fuzzy Inference System (IGANFIS) model with RMSE of 0. 02795 m3 s-1 and NSE of 0. 94467 were able to predict the discharge values of all hydrometric stations of the Chalus River watershed with a very low error and high accuracy. The results of cross validation stage confirmed the efficiency of models. Hydro-climatic variables and geomorphologic parameters selected in the study were: discharge of one day ago, discharge of two days ago, rainfall of current day and rainfall of one day ago and S, CN and A, respectively. In addition, the IGANN model shows superiority compared with the IGANFIS model.

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APA: Copy

GHOLAMI, SHABANALI, VAFAKHAH, MEHDI, Ghaderi, Kamal, & JAVADI, MOHAMMAD REZA. (2020). Simulation of (ANFIS) rainfall-runoff process using geomorphology based adaptive neuro- fuzzy inference system. CASPIAN JOURNAL OF ENVIRONMENTAL SCIENCES (CJES), 18(2 ), 109-122. SID. https://sid.ir/paper/378430/en

Vancouver: Copy

GHOLAMI SHABANALI, VAFAKHAH MEHDI, Ghaderi Kamal, JAVADI MOHAMMAD REZA. Simulation of (ANFIS) rainfall-runoff process using geomorphology based adaptive neuro- fuzzy inference system. CASPIAN JOURNAL OF ENVIRONMENTAL SCIENCES (CJES)[Internet]. 2020;18(2 ):109-122. Available from: https://sid.ir/paper/378430/en

IEEE: Copy

SHABANALI GHOLAMI, MEHDI VAFAKHAH, Kamal Ghaderi, and MOHAMMAD REZA JAVADI, “Simulation of (ANFIS) rainfall-runoff process using geomorphology based adaptive neuro- fuzzy inference system,” CASPIAN JOURNAL OF ENVIRONMENTAL SCIENCES (CJES), vol. 18, no. 2 , pp. 109–122, 2020, [Online]. Available: https://sid.ir/paper/378430/en

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