Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

Nurul Najihah, Che Razali and Ngahzaifa, Ab. Ghani and Syifak Izhar, Hisham and Shahreen, Kasim and Widodo, Nuryono Satya and Sutikno, Tole (2020) Rainfall-runoff modelling using adaptive neuro-fuzzy inference system. Indonesian Journal of Electrical Engineering and Computer Science, 17 (2). pp. 1117-1126. ISSN 2502-4752. (Published)

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This paper discusses the working mechanism of ANFIS, the flow of research, the implementation and evaluation of ANFIS models, and discusses the pros and cons of each option of input parameters applied, in order to solve the problem of rainfall-runoff forecasting. The rainfall-runoff modelling considers time-series data of rainfall amount (in mm) and water discharge amount (in m3/s). For model parameters, the models apply three triangle membership functions for each input. Meanwhile, the accuracy of the data is measured using the Root Mean Square Error (RMSE). Models with good performance in training have low values of RMSE. Hence, the 4-input model data is the best model to measure prediction accurately with the value of RMSE as 22.157. It is proven that ANFIS has the potential to be used for flood forecasting generally, or rainfall-runoff modelling specifically.

Item Type: Article
Uncontrolled Keywords: Rainfall-runoff; ANFIS; Artificial intelligence; Forecasting
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Institute of Postgraduate Studies
Depositing User: Dr. Syifak Izhar Hisham
Date Deposited: 18 Feb 2020 06:53
Last Modified: 18 Feb 2020 06:53
URI: http://umpir.ump.edu.my/id/eprint/27154
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