Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset

ANFIS (Adaptive Neuro Fuzzy Inference System), for its advantages of having linguistic representation of models has been the interests of both hydrological operational modellers and scientists/theorists. In hydrology especially, every process is unique and dependent on large number of natural factor...

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Main Authors:	Ngahzaifa, Ab. Ghani, Zuriani, Mustaffa, Zafril Rizal, M Azmi
Format:	Article
Language:	English
Published:	American Scientific Publisher 2018
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/19447/ http://umpir.ump.edu.my/id/eprint/19447/ http://umpir.ump.edu.my/id/eprint/19447/1/53.%20Visual%20analysis%20to%20investigate%20the%20capability%20of%20ANFIS%20in%20modelling%20hydrological%20relationship%20using%20synthetic%20dataset1.pdf

id	ump-19447
recordtype	eprints
spelling	ump-194472018-11-22T03:15:56Z http://umpir.ump.edu.my/id/eprint/19447/ Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset Ngahzaifa, Ab. Ghani Zuriani, Mustaffa Zafril Rizal, M Azmi QA76 Computer software ANFIS (Adaptive Neuro Fuzzy Inference System), for its advantages of having linguistic representation of models has been the interests of both hydrological operational modellers and scientists/theorists. In hydrology especially, every process is unique and dependent on large number of natural factors hence modelling using machine learning algorithm without considering hydrological insight is very dangerous. In using most of the machine learning algorithms including ANFIS, to obtain the best model, the common and normal approach is always by comparing models of different parameter settings based on the goodness-offit statistical measures. This approach is not always accurate, as each statistical measure has its drawbacks in terms of how accurate it is presenting the model depending on the condition and complexity of the data involved. This research proposes the use of synthetic data in order to explore and understand the behaviour of ANFIS parameters on hydrological data, specifically hysteresis effect in sediment-discharge relationship, in order to improve the efficiency of the modelling process. The results of simulation on the synthetic datasets are then presented and analysed visually. The modelling process is then repeated on real datasets in order to validate the findings. American Scientific Publisher 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/19447/1/53.%20Visual%20analysis%20to%20investigate%20the%20capability%20of%20ANFIS%20in%20modelling%20hydrological%20relationship%20using%20synthetic%20dataset1.pdf Ngahzaifa, Ab. Ghani and Zuriani, Mustaffa and Zafril Rizal, M Azmi (2018) Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset. Advanced Science Letters, 24 (10). pp. 7617-7622. ISSN 1936-6612 https://doi.org/10.1166/asl.2018.12989
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Ngahzaifa, Ab. Ghani Zuriani, Mustaffa Zafril Rizal, M Azmi Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset
description	ANFIS (Adaptive Neuro Fuzzy Inference System), for its advantages of having linguistic representation of models has been the interests of both hydrological operational modellers and scientists/theorists. In hydrology especially, every process is unique and dependent on large number of natural factors hence modelling using machine learning algorithm without considering hydrological insight is very dangerous. In using most of the machine learning algorithms including ANFIS, to obtain the best model, the common and normal approach is always by comparing models of different parameter settings based on the goodness-offit statistical measures. This approach is not always accurate, as each statistical measure has its drawbacks in terms of how accurate it is presenting the model depending on the condition and complexity of the data involved. This research proposes the use of synthetic data in order to explore and understand the behaviour of ANFIS parameters on hydrological data, specifically hysteresis effect in sediment-discharge relationship, in order to improve the efficiency of the modelling process. The results of simulation on the synthetic datasets are then presented and analysed visually. The modelling process is then repeated on real datasets in order to validate the findings.
format	Article
author	Ngahzaifa, Ab. Ghani Zuriani, Mustaffa Zafril Rizal, M Azmi
author_facet	Ngahzaifa, Ab. Ghani Zuriani, Mustaffa Zafril Rizal, M Azmi
author_sort	Ngahzaifa, Ab. Ghani
title	Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset
title_short	Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset
title_full	Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset
title_fullStr	Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset
title_full_unstemmed	Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset
title_sort	visual analysis to investigate the capability of anfis in modelling hydrological relationship using synthetic dataset
publisher	American Scientific Publisher
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/19447/ http://umpir.ump.edu.my/id/eprint/19447/ http://umpir.ump.edu.my/id/eprint/19447/1/53.%20Visual%20analysis%20to%20investigate%20the%20capability%20of%20ANFIS%20in%20modelling%20hydrological%20relationship%20using%20synthetic%20dataset1.pdf
first_indexed	2023-09-18T22:27:45Z
last_indexed	2023-09-18T22:27:45Z
_version_	1777416080054026240

Visual analysis to investigate the capability of ANFIS in modelling hydrological relationship using synthetic dataset

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