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Evaluation of daily rainfall-runoff model using multilayer perceptron and particle swarm optimization feed forward neural networks

In recent years, Artificial Neural Networks (ANNs) have been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible, and are able to extract the relation between th...

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Main Authors: Kuok, Kuok Kin, Harun, Sobri, Shamsuddin, Siti Mariyam, Chiu, P.
Format: Article
Published: International Association for Environmental Hydrology 2010
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/26144/
http://hydroweb.com/journal-hydrology-2010-paper-10.html
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spelling my.utm.261442018-10-31T12:19:54Z http://eprints.utm.my/id/eprint/26144/ Evaluation of daily rainfall-runoff model using multilayer perceptron and particle swarm optimization feed forward neural networks Kuok, Kuok Kin Harun, Sobri Shamsuddin, Siti Mariyam Chiu, P. TA Engineering (General). Civil engineering (General) In recent years, Artificial Neural Networks (ANNs) have been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible, and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. ANNs with sufficient hidden units are able to approximate any continuous function to any degree of accuracy by performing efficient training. In this study, two types of ANNs, namely, the multilayer perceptron neural network (MLP) and the newly developed particle swarm optimization feedforward neural network (PSONN) are applied to model the daily rainfall-runoff relationship for the Bedup Basin, Sarawak, Malaysia. Various models are investigated in searching for the optimal configuration of ANNs. Results are evaluated using the coefficient of correlation (R) and the nash-sutcliffe coefficient (E2). With the input data of current rainfall, antecedent rainfall and antecedent runoff, MLP simulated the current runoff perfectly for training with R=1.000 and E2=1.000, and R=0.911 and E2=0.8155 for testing data set. Meanwhile, PSONN also simulated the current runoff accurately with R=0.872 and E2=0.7754 for training data set, and R=0.900 and E2=0.8067 for testing data set. Thus, it can be concluded that ANNs are able to model the rainfall-runoff relationship accurately. The performance of the newly developed PSONN is comparable with the well-known MLP network, which had been successfully used to model rainfall-runoff for the Bedup Basin. International Association for Environmental Hydrology 2010 Article PeerReviewed Kuok, Kuok Kin and Harun, Sobri and Shamsuddin, Siti Mariyam and Chiu, P. (2010) Evaluation of daily rainfall-runoff model using multilayer perceptron and particle swarm optimization feed forward neural networks. Journal of Environmental Hydrology, 18 . 1 -16. ISSN 1058-3912 http://hydroweb.com/journal-hydrology-2010-paper-10.html
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/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Kuok, Kuok Kin
Harun, Sobri
Shamsuddin, Siti Mariyam
Chiu, P.
Evaluation of daily rainfall-runoff model using multilayer perceptron and particle swarm optimization feed forward neural networks
description In recent years, Artificial Neural Networks (ANNs) have been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible, and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. ANNs with sufficient hidden units are able to approximate any continuous function to any degree of accuracy by performing efficient training. In this study, two types of ANNs, namely, the multilayer perceptron neural network (MLP) and the newly developed particle swarm optimization feedforward neural network (PSONN) are applied to model the daily rainfall-runoff relationship for the Bedup Basin, Sarawak, Malaysia. Various models are investigated in searching for the optimal configuration of ANNs. Results are evaluated using the coefficient of correlation (R) and the nash-sutcliffe coefficient (E2). With the input data of current rainfall, antecedent rainfall and antecedent runoff, MLP simulated the current runoff perfectly for training with R=1.000 and E2=1.000, and R=0.911 and E2=0.8155 for testing data set. Meanwhile, PSONN also simulated the current runoff accurately with R=0.872 and E2=0.7754 for training data set, and R=0.900 and E2=0.8067 for testing data set. Thus, it can be concluded that ANNs are able to model the rainfall-runoff relationship accurately. The performance of the newly developed PSONN is comparable with the well-known MLP network, which had been successfully used to model rainfall-runoff for the Bedup Basin.
format Article
author Kuok, Kuok Kin
Harun, Sobri
Shamsuddin, Siti Mariyam
Chiu, P.
author_facet Kuok, Kuok Kin
Harun, Sobri
Shamsuddin, Siti Mariyam
Chiu, P.
author_sort Kuok, Kuok Kin
title Evaluation of daily rainfall-runoff model using multilayer perceptron and particle swarm optimization feed forward neural networks
title_short Evaluation of daily rainfall-runoff model using multilayer perceptron and particle swarm optimization feed forward neural networks
title_full Evaluation of daily rainfall-runoff model using multilayer perceptron and particle swarm optimization feed forward neural networks
title_fullStr Evaluation of daily rainfall-runoff model using multilayer perceptron and particle swarm optimization feed forward neural networks
title_full_unstemmed Evaluation of daily rainfall-runoff model using multilayer perceptron and particle swarm optimization feed forward neural networks
title_sort evaluation of daily rainfall-runoff model using multilayer perceptron and particle swarm optimization feed forward neural networks
publisher International Association for Environmental Hydrology
publishDate 2010
url http://eprints.utm.my/id/eprint/26144/
http://hydroweb.com/journal-hydrology-2010-paper-10.html
_version_ 1643647692118687744
score 13.149126

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