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...

全面介紹

Saved in:
書目詳細資料
主要作者: Kuok, King Kuok
格式: Thesis
語言:English
出版: 2004
主題:
在線閱讀:http://ir.unimas.my/id/eprint/3137/1/Kuok.pdf
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
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