Indonesian Journal of Electrical Engineering and Computer Science
Vol 17, No 2: February 2020

Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

Nurul Najihah Che Razali (Universiti Malaysia Pahang)
Ngahzaifa Ab. Ghani (Universiti Malaysia Pahang)
Syifak Izhar Hisham (Universiti Malaysia Pahang)
Shahreen Kasim (Universiti Tun Hussein Onn Malaysia)
Nuryono Satya Widodo (Universitas Ahmad Dahlan)
Tole Sutikno (Universitas Ahmad Dahlan)



Article Info

Publish Date
01 Feb 2020

Abstract

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.

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