Parameter optimization methods for calibrating tank model and neural network for rainfall-runoff modelling

The transformation of rainfall into runoff involves many highly complex hydrological components that require various hydrological data and topographical information. These data are hard to obtain and not consistent. Therefore, hydrologic tank and artificial neural networks models that require only r...

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Main Author:	Kuok, King Kuok
Format:	Thesis
Language:	English
Published:	2010
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/36283/1/KuokKingKuokPFKA2010.pdf
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id	my-utm-ep.36283
record_format	uketd_dc
spelling	my-utm-ep.362832020-02-09T04:41:16Z Parameter optimization methods for calibrating tank model and neural network for rainfall-runoff modelling 2010 Kuok, King Kuok TA Engineering (General). Civil engineering (General) The transformation of rainfall into runoff involves many highly complex hydrological components that require various hydrological data and topographical information. These data are hard to obtain and not consistent. Therefore, hydrologic tank and artificial neural networks models that require only rainfall and runoff data were proposed. The selected study area is Bedup Basin, Sarawak, Malaysia, a rural catchment in humid region. A new global optimization method named as particle swarm optimization (PSO) was proposed, and compared with shuffle complex evolution and genetic algorithm techniques for calibrating the tank models’ parameters automatically. PSO is also hybrid with neural network to form particle swarm optimization feedforward neural network (PSONN) to overcome the slow convergence rate and trapping at local minima problems. PSONN performance is then compared with multilayer perceptron and recurrent networks, that used backpropagation algorithm. Models performances are measured using coefficient of correlation (R) and Nash-Sutcliffe coefficient (E2). Generally, artificial neural networks performance is slightly better than tank model. Results of tank model calibration indicate that PSO method appeared to be the best based on its robustness, reliability, efficiency, accuracy and smallest variability in boxplots. Shuffle complex evolution follows as the second best and the third best is genetic algorithm for both daily and hourly runoff simulation. Among multilayer perceptron, recurrent and PSONN investigated, recurrent network forecasts daily and hourly runoff most accurately, followed second best by multilayer perceptron and lastly PSONN. PSONN has proven its remarkable capability to simulate daily and hourly runoff with an acceptable accuracy. This study revealed that artificial intelligence methods especially PSO, have offered a real prospect for an efficient, simple, cheaper, more flexible, and well suited to model flood processes. 2010 Thesis http://eprints.utm.my/id/eprint/36283/ http://eprints.utm.my/id/eprint/36283/1/KuokKingKuokPFKA2010.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:90602?queryType=vitalDismax&query=Parameter+optimization+methods+for+calibrating+tank+model+and+neural+network+for+rainfall-runoff+modelling&public=true phd doctoral Universiti Teknologi Malaysia, Faculty of Civil Engineering Faculty of Civil Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Kuok, King Kuok Parameter optimization methods for calibrating tank model and neural network for rainfall-runoff modelling
description	The transformation of rainfall into runoff involves many highly complex hydrological components that require various hydrological data and topographical information. These data are hard to obtain and not consistent. Therefore, hydrologic tank and artificial neural networks models that require only rainfall and runoff data were proposed. The selected study area is Bedup Basin, Sarawak, Malaysia, a rural catchment in humid region. A new global optimization method named as particle swarm optimization (PSO) was proposed, and compared with shuffle complex evolution and genetic algorithm techniques for calibrating the tank models’ parameters automatically. PSO is also hybrid with neural network to form particle swarm optimization feedforward neural network (PSONN) to overcome the slow convergence rate and trapping at local minima problems. PSONN performance is then compared with multilayer perceptron and recurrent networks, that used backpropagation algorithm. Models performances are measured using coefficient of correlation (R) and Nash-Sutcliffe coefficient (E2). Generally, artificial neural networks performance is slightly better than tank model. Results of tank model calibration indicate that PSO method appeared to be the best based on its robustness, reliability, efficiency, accuracy and smallest variability in boxplots. Shuffle complex evolution follows as the second best and the third best is genetic algorithm for both daily and hourly runoff simulation. Among multilayer perceptron, recurrent and PSONN investigated, recurrent network forecasts daily and hourly runoff most accurately, followed second best by multilayer perceptron and lastly PSONN. PSONN has proven its remarkable capability to simulate daily and hourly runoff with an acceptable accuracy. This study revealed that artificial intelligence methods especially PSO, have offered a real prospect for an efficient, simple, cheaper, more flexible, and well suited to model flood processes.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Kuok, King Kuok
author_facet	Kuok, King Kuok
author_sort	Kuok, King Kuok
title	Parameter optimization methods for calibrating tank model and neural network for rainfall-runoff modelling
title_short	Parameter optimization methods for calibrating tank model and neural network for rainfall-runoff modelling
title_full	Parameter optimization methods for calibrating tank model and neural network for rainfall-runoff modelling
title_fullStr	Parameter optimization methods for calibrating tank model and neural network for rainfall-runoff modelling
title_full_unstemmed	Parameter optimization methods for calibrating tank model and neural network for rainfall-runoff modelling
title_sort	parameter optimization methods for calibrating tank model and neural network for rainfall-runoff modelling
granting_institution	Universiti Teknologi Malaysia, Faculty of Civil Engineering
granting_department	Faculty of Civil Engineering
publishDate	2010
url	http://eprints.utm.my/id/eprint/36283/1/KuokKingKuokPFKA2010.pdf
_version_	1747816424032698368

Parameter optimization methods for calibrating tank model and neural network for rainfall-runoff modelling

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