An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction

The use of data-driven modelling (DDM) in hydrological forecasting has been in practice since decades ago. Nevertheless, despite the ease of use, DDM approach has also been criticised due its 'black box' nature where the physical insights of the modelled processes are far from reach. Whil...

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Main Author:	Ngahzaifa, Ab. Ghani
Format:	Thesis
Language:	English
Published:	2012
Subjects:	GB Physical geography
Online Access:	http://umpir.ump.edu.my/id/eprint/8774/ http://umpir.ump.edu.my/id/eprint/8774/ http://umpir.ump.edu.my/id/eprint/8774/1/NGAHZAIFA%20AB%20GHANI.PDF

id	ump-8774
recordtype	eprints
spelling	ump-87742019-07-17T01:56:45Z http://umpir.ump.edu.my/id/eprint/8774/ An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction Ngahzaifa, Ab. Ghani GB Physical geography The use of data-driven modelling (DDM) in hydrological forecasting has been in practice since decades ago. Nevertheless, despite the ease of use, DDM approach has also been criticised due its 'black box' nature where the physical insights of the modelled processes are far from reach. Whilst hydrologists are craving for the insight, the operational modellers are and will always prefer an easily applicable method regardless of whether the model is able to deliver knowledge. Hence, a method that could fulfil the need of both would be a perfect solution, ANFIS (Adaptive Neuro Fuzzy Inference System), for its advantages of having linguistic representation of models has been the interests of both groups and have been successfully tested on a number of international catchments. It is however still unclear as to what extent is ANFIS able to deliver the required knowledge; how capable is ANFIS in modelling sediment-discharge; and what are the advantages and disadvantages of using ANFIS as a modelling tool. This thesis explores ANFIS capability in order to address these issues. The methods involved include creating synthetic datasets that mimic the sediment-discharge relationships; experimenting with different ANFIS parameter settings; and observing and analysing the behaviour of models with the help of statistical and graphical evaluations. The results highlight that ANFIS is capable to model most of the tested relationships, but the produced model is very dependent on the parameters applied when training the model. Wrong choice of parameters may lead to the production of models with good metrics but low transferability, or even worse, not transferable at all. As a conclusion, ANFIS can be used in sediment-discharge modelling with certain restrictions on training parameters but this is mostly applicable to the simple and common rating curves. More studies are needed in order to explore the potential of ANFIS to model complex sediment-discharge processes. 2012 Thesis NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/8774/1/NGAHZAIFA%20AB%20GHANI.PDF Ngahzaifa, Ab. Ghani (2012) An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction. PhD thesis, University of Nottingham. http://iportal.ump.edu.my/lib/item?id=chamo:83985&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	GB Physical geography
spellingShingle	GB Physical geography Ngahzaifa, Ab. Ghani An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction
description	The use of data-driven modelling (DDM) in hydrological forecasting has been in practice since decades ago. Nevertheless, despite the ease of use, DDM approach has also been criticised due its 'black box' nature where the physical insights of the modelled processes are far from reach. Whilst hydrologists are craving for the insight, the operational modellers are and will always prefer an easily applicable method regardless of whether the model is able to deliver knowledge. Hence, a method that could fulfil the need of both would be a perfect solution, ANFIS (Adaptive Neuro Fuzzy Inference System), for its advantages of having linguistic representation of models has been the interests of both groups and have been successfully tested on a number of international catchments. It is however still unclear as to what extent is ANFIS able to deliver the required knowledge; how capable is ANFIS in modelling sediment-discharge; and what are the advantages and disadvantages of using ANFIS as a modelling tool. This thesis explores ANFIS capability in order to address these issues. The methods involved include creating synthetic datasets that mimic the sediment-discharge relationships; experimenting with different ANFIS parameter settings; and observing and analysing the behaviour of models with the help of statistical and graphical evaluations. The results highlight that ANFIS is capable to model most of the tested relationships, but the produced model is very dependent on the parameters applied when training the model. Wrong choice of parameters may lead to the production of models with good metrics but low transferability, or even worse, not transferable at all. As a conclusion, ANFIS can be used in sediment-discharge modelling with certain restrictions on training parameters but this is mostly applicable to the simple and common rating curves. More studies are needed in order to explore the potential of ANFIS to model complex sediment-discharge processes.
format	Thesis
author	Ngahzaifa, Ab. Ghani
author_facet	Ngahzaifa, Ab. Ghani
author_sort	Ngahzaifa, Ab. Ghani
title	An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction
title_short	An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction
title_full	An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction
title_fullStr	An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction
title_full_unstemmed	An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction
title_sort	evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/8774/ http://umpir.ump.edu.my/id/eprint/8774/ http://umpir.ump.edu.my/id/eprint/8774/1/NGAHZAIFA%20AB%20GHANI.PDF
first_indexed	2023-09-18T22:06:42Z
last_indexed	2023-09-18T22:06:42Z
_version_	1777414756591730688

An evaluation of the potential of adaptive neuro-fuzzy inference system in hydrological modelling and prediction

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