Rainfall-runoff modelling using artificial neural network method

Rainfall and surface runoff are the driving forces behind all stormwater studies and designs. The relationship is known to be highly non-linear and complex that is dependent on numerous factors. In order to overcome the problems on the non-linearity and lack of information in rainfall-runoff modelli...

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Main Author:	Ahmat Nor, Nor Irwan
Format:	Thesis
Language:	English
Published:	2005
Subjects:	TA Engineering (General) Civil engineering (General) QA76 Computer software
Online Access:	http://eprints.utm.my/id/eprint/4307/1/NorIrwanAhmatNorPFKA2005.pdf
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id	my-utm-ep.4307
record_format	uketd_dc
spelling	my-utm-ep.43072018-01-17T00:23:30Z Rainfall-runoff modelling using artificial neural network method 2005-08 Ahmat Nor, Nor Irwan TA Engineering (General). Civil engineering (General) QA76 Computer software Rainfall and surface runoff are the driving forces behind all stormwater studies and designs. The relationship is known to be highly non-linear and complex that is dependent on numerous factors. In order to overcome the problems on the non-linearity and lack of information in rainfall-runoff modelling, this study introduced the Artificial Neural Network (ANN) approach to model the dynamic of rainfall-runoff processes. The ANN method behaved as the black-box model and proven could handle the non-linearity processes in complex system. Numerous structures of ANN models were designed to determine the relationship between the daily and hourly rainfall against corresponding runoff. Therefore, the desired runoff could be predicted using the rainfall data, based on the relationship established by the ANN training computation. The ANN architecture is simple and it considers only the rainfall and runoff data as variables. The internal processes that control the rainfall to runoff transformation will be translated into ANN weights. Once the architecture of the network is defined, weights are calculated so as to represent the desired output through a learning process where the ANN is trained to obtain the expected results. Two types of ANN architectures are recommended and they are namely the multilayer perceptron (MLP) and radial basis function (RBF) networks. Several catchments such as Sungai Bekok, Sungai Ketil, Sungai Klang and Sungai Slim were selected to test the methodology. The model performance was evaluated by comparing to the actual observed flow series. Further, the ANN results were compared against the results produced from the application of HEC-HMS, XP-SWMM and multiple linear regression (MLR). It had been found that the ANN could predict runoff accurately, with good correlation between the observed and predicted values compared to the MLR, XP-SWMM and HEC-HMS models. Obviously, the ANN application to model the daily and hourly streamflow hydrograph was successful. 2005-08 Thesis http://eprints.utm.my/id/eprint/4307/ http://eprints.utm.my/id/eprint/4307/1/NorIrwanAhmatNorPFKA2005.pdf application/pdf en public 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) QA76 Computer software
spellingShingle	TA Engineering (General) Civil engineering (General) QA76 Computer software Ahmat Nor, Nor Irwan Rainfall-runoff modelling using artificial neural network method
description	Rainfall and surface runoff are the driving forces behind all stormwater studies and designs. The relationship is known to be highly non-linear and complex that is dependent on numerous factors. In order to overcome the problems on the non-linearity and lack of information in rainfall-runoff modelling, this study introduced the Artificial Neural Network (ANN) approach to model the dynamic of rainfall-runoff processes. The ANN method behaved as the black-box model and proven could handle the non-linearity processes in complex system. Numerous structures of ANN models were designed to determine the relationship between the daily and hourly rainfall against corresponding runoff. Therefore, the desired runoff could be predicted using the rainfall data, based on the relationship established by the ANN training computation. The ANN architecture is simple and it considers only the rainfall and runoff data as variables. The internal processes that control the rainfall to runoff transformation will be translated into ANN weights. Once the architecture of the network is defined, weights are calculated so as to represent the desired output through a learning process where the ANN is trained to obtain the expected results. Two types of ANN architectures are recommended and they are namely the multilayer perceptron (MLP) and radial basis function (RBF) networks. Several catchments such as Sungai Bekok, Sungai Ketil, Sungai Klang and Sungai Slim were selected to test the methodology. The model performance was evaluated by comparing to the actual observed flow series. Further, the ANN results were compared against the results produced from the application of HEC-HMS, XP-SWMM and multiple linear regression (MLR). It had been found that the ANN could predict runoff accurately, with good correlation between the observed and predicted values compared to the MLR, XP-SWMM and HEC-HMS models. Obviously, the ANN application to model the daily and hourly streamflow hydrograph was successful.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ahmat Nor, Nor Irwan
author_facet	Ahmat Nor, Nor Irwan
author_sort	Ahmat Nor, Nor Irwan
title	Rainfall-runoff modelling using artificial neural network method
title_short	Rainfall-runoff modelling using artificial neural network method
title_full	Rainfall-runoff modelling using artificial neural network method
title_fullStr	Rainfall-runoff modelling using artificial neural network method
title_full_unstemmed	Rainfall-runoff modelling using artificial neural network method
title_sort	rainfall-runoff modelling using artificial neural network method
granting_institution	Universiti Teknologi Malaysia, Faculty of Civil Engineering
granting_department	Faculty of Civil Engineering
publishDate	2005
url	http://eprints.utm.my/id/eprint/4307/1/NorIrwanAhmatNorPFKA2005.pdf
_version_	1747814512074948608

Rainfall-runoff modelling using artificial neural network method

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