Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area

Artificial Neural Network (ANN) is an information-processing system composed of many nonlinear and densely interconnected processing elements or neurons. ANN is able to extract the relation between the inputs and outputs of a process, without the physics being explicitly provided to them. The natura...

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Bibliographic Details
Main Author:	Kuok, King Kuok
Format:	Thesis
Language:	English
Published:	2004
Subjects:	TC Hydraulic engineering Ocean engineering
Online Access:	http://ir.unimas.my/id/eprint/3137/1/Kuok.pdf
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id	my-unimas-ir.3137
record_format	uketd_dc
spelling	my-unimas-ir.31372023-06-20T07:50:50Z Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area 2004 Kuok, King Kuok TC Hydraulic engineering. Ocean engineering Artificial Neural Network (ANN) is an information-processing system composed of many nonlinear and densely interconnected processing elements or neurons. ANN is able to extract the relation between the inputs and outputs of a process, without the physics being explicitly provided to them. The natural behavior of hydrological processes is appropriate for the application ANN in hydrology. A rainfall runoff model for Sungai Bedup Basin in Sarawak was built using three different ANN architectures namely Multilayer perceptron (MLP), Recurrent (REC) and Radial Basic function (RBF). Universiti Malaysia Sarawak, UNIMAS 2004 Thesis http://ir.unimas.my/id/eprint/3137/ http://ir.unimas.my/id/eprint/3137/1/Kuok.pdf text en validuser masters Universiti Malaysia Sarawak (UNIMAS) Faculty of Engineering
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	TC Hydraulic engineering Ocean engineering
spellingShingle	TC Hydraulic engineering Ocean engineering Kuok, King Kuok Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area
description	Artificial Neural Network (ANN) is an information-processing system composed of many nonlinear and densely interconnected processing elements or neurons. ANN is able to extract the relation between the inputs and outputs of a process, without the physics being explicitly provided to them. The natural behavior of hydrological processes is appropriate for the application ANN in hydrology. A rainfall runoff model for Sungai Bedup Basin in Sarawak was built using three different ANN architectures namely Multilayer perceptron (MLP), Recurrent (REC) and Radial Basic function (RBF).
format	Thesis
qualification_level	Master's degree
author	Kuok, King Kuok
author_facet	Kuok, King Kuok
author_sort	Kuok, King Kuok
title	Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area
title_short	Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area
title_full	Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area
title_fullStr	Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area
title_full_unstemmed	Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area
title_sort	artificial neural networks for rainfall runoff modelling with special reference to sg. bedup catchment area
granting_institution	Universiti Malaysia Sarawak (UNIMAS)
granting_department	Faculty of Engineering
publishDate	2004
url	http://ir.unimas.my/id/eprint/3137/1/Kuok.pdf
_version_	1783727911482687488

Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area

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