Rainfall-Runoff Modeling by Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Variable Linear Regression (MLR)

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Kia Isa; EMADI ALIREZA; Gholami Sefidkohi Mohammadali

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Journal Paper

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Journal: Journal of Irrigation and Water Engineering Year:2019 | Volume:9 | Issue:36 Page(s): 39-51

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Information Journal Paper

Title

Rainfall-Runoff Modeling by Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Variable Linear Regression (MLR)

Author(s)

Kia Isa | EMADI ALIREZA | Gholami Sefidkohi Mohammadali | Issue Writer Certificate

Keywords

antecedent precipitationQ1

efficiencyQ1

flow rateQ2

forecastQ1

membership functionQ1

Abstract

In this research, the efficiency of Neuro-Fuzzy System was evaluated for estimating runoff in the mountainous area of the Haraz watershed. The goal is to create a model with proper functions and membership degrees that can properly estimate the relationship between rainfall and runoff in either basin or watershed. Thus, to predict the amount of runoff, 44 different combinations of rainfall, temperature, evaporation, flow rate and antecedent precipitation index with lag time entered to an ANFIS model during the period of 32 years as a daily data. Among the different combinations of input, the structure of rainfall and mean temperature of current day with moisture index and flow rate of 1-day ago was selected as the appropriate model. The results of t-test for determining the significant difference showed that there is no significant difference between ANFIS and MLR methods. The ANFIS system with a two degree triangular membership function with RMSE=2. 67, VE=4. 16, R2=0. 98 and EF=0. 97 compared to the MLR with RMSE=2. 83, VE=4. 55, R2=0. 94 and EF=0. 92 during the test period, has a better performance in rainfall-runoff modeling of the mountainous area of the Haraz watershed.

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APA: Copy

Kia, Isa, EMADI, ALIREZA, & Gholami Sefidkohi, Mohammadali. (2019). Rainfall-Runoff Modeling by Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Variable Linear Regression (MLR). IRANIAN OF IRRIGATION & WATER ENGINEERING, 9(36 ), 39-51. SID. https://sid.ir/paper/247250/en

Vancouver: Copy

Kia Isa, EMADI ALIREZA, Gholami Sefidkohi Mohammadali. Rainfall-Runoff Modeling by Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Variable Linear Regression (MLR). IRANIAN OF IRRIGATION & WATER ENGINEERING[Internet]. 2019;9(36 ):39-51. Available from: https://sid.ir/paper/247250/en

IEEE: Copy

Isa Kia, ALIREZA EMADI, and Mohammadali Gholami Sefidkohi, “Rainfall-Runoff Modeling by Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Variable Linear Regression (MLR),” IRANIAN OF IRRIGATION & WATER ENGINEERING, vol. 9, no. 36 , pp. 39–51, 2019, [Online]. Available: https://sid.ir/paper/247250/en

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