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Rainfall - runoff modeling 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)
Unspecified
Online Access:http://eprints.utm.my/id/eprint/18659/1/NorIrwanAhmadNorPFKA2005.pdf
http://eprints.utm.my/id/eprint/18659/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62406?queryType=vitalDismax&query=modeling+using+artificial+neural+network+method+&public=true
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spelling my.utm.186592017-10-11T04:24:41Z http://eprints.utm.my/id/eprint/18659/ Rainfall - runoff modeling using artificial neural network method Ahmat Nor, Nor Irwan TA Engineering (General). Civil engineering (General) Unspecified 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/18659/1/NorIrwanAhmadNorPFKA2005.pdf Ahmat Nor, Nor Irwan (2005) Rainfall - runoff modeling using artificial neural network method. PhD thesis, Universiti Teknologi Malaysia, Faculty of Civil engineering. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62406?queryType=vitalDismax&query=modeling+using+artificial+neural+network+method+&public=true
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
Unspecified
spellingShingle TA Engineering (General). Civil engineering (General)
Unspecified
Ahmat Nor, Nor Irwan
Rainfall - runoff modeling 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
author Ahmat Nor, Nor Irwan
author_facet Ahmat Nor, Nor Irwan
author_sort Ahmat Nor, Nor Irwan
title Rainfall - runoff modeling using artificial neural network method
title_short Rainfall - runoff modeling using artificial neural network method
title_full Rainfall - runoff modeling using artificial neural network method
title_fullStr Rainfall - runoff modeling using artificial neural network method
title_full_unstemmed Rainfall - runoff modeling using artificial neural network method
title_sort rainfall - runoff modeling using artificial neural network method
publishDate 2005
url http://eprints.utm.my/id/eprint/18659/1/NorIrwanAhmadNorPFKA2005.pdf
http://eprints.utm.my/id/eprint/18659/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62406?queryType=vitalDismax&query=modeling+using+artificial+neural+network+method+&public=true
_version_ 1643646963253510144
score 13.211869

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