Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

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 consi...

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Main Authors: Nurul Najihah, Che Razali, Ngahzaifa, Ab. Ghani, Syifak Izhar, Hisham, Shahreen, Kasim, Widodo, Nuryono Satya, Sutikno, Tole
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2020
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/27154/
http://umpir.ump.edu.my/id/eprint/27154/
http://umpir.ump.edu.my/id/eprint/27154/
http://umpir.ump.edu.my/id/eprint/27154/1/21145-40184-1-PB.pdf
id ump-27154
recordtype eprints
spelling ump-271542020-02-18T06:53:26Z http://umpir.ump.edu.my/id/eprint/27154/ Rainfall-runoff modelling using adaptive neuro-fuzzy inference system Nurul Najihah, Che Razali Ngahzaifa, Ab. Ghani Syifak Izhar, Hisham Shahreen, Kasim Widodo, Nuryono Satya Sutikno, Tole QA75 Electronic computers. Computer science 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. Institute of Advanced Engineering and Science 2020-02 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/27154/1/21145-40184-1-PB.pdf 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 http://doi.org/10.11591/ijeecs.v17.i2.pp1117-1126 http://doi.org/10.11591/ijeecs.v17.i2.pp1117-1126
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Nurul Najihah, Che Razali
Ngahzaifa, Ab. Ghani
Syifak Izhar, Hisham
Shahreen, Kasim
Widodo, Nuryono Satya
Sutikno, Tole
Rainfall-runoff modelling using adaptive neuro-fuzzy inference system
description 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.
format Article
author Nurul Najihah, Che Razali
Ngahzaifa, Ab. Ghani
Syifak Izhar, Hisham
Shahreen, Kasim
Widodo, Nuryono Satya
Sutikno, Tole
author_facet Nurul Najihah, Che Razali
Ngahzaifa, Ab. Ghani
Syifak Izhar, Hisham
Shahreen, Kasim
Widodo, Nuryono Satya
Sutikno, Tole
author_sort Nurul Najihah, Che Razali
title Rainfall-runoff modelling using adaptive neuro-fuzzy inference system
title_short Rainfall-runoff modelling using adaptive neuro-fuzzy inference system
title_full Rainfall-runoff modelling using adaptive neuro-fuzzy inference system
title_fullStr Rainfall-runoff modelling using adaptive neuro-fuzzy inference system
title_full_unstemmed Rainfall-runoff modelling using adaptive neuro-fuzzy inference system
title_sort rainfall-runoff modelling using adaptive neuro-fuzzy inference system
publisher Institute of Advanced Engineering and Science
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/27154/
http://umpir.ump.edu.my/id/eprint/27154/
http://umpir.ump.edu.my/id/eprint/27154/
http://umpir.ump.edu.my/id/eprint/27154/1/21145-40184-1-PB.pdf
first_indexed 2023-09-18T22:42:35Z
last_indexed 2023-09-18T22:42:35Z
_version_ 1777417013839265792

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