Particle swarm optimization feedforward neural network for modeling runoff
The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy a...
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| Format: | Article |
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IJENS Publishers
2010
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| _version_ | 1796856213264138240 |
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| author | Kuok, K. K. Harun, Sobri Shamsuddin, Siti Mariyam |
| author_facet | Kuok, K. K. Harun, Sobri Shamsuddin, Siti Mariyam |
| author_sort | Kuok, K. K. |
| collection | ePrints |
| description | The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. However, the backpropagation neural network convergence rate is relatively slow and solutions can be trapped at local minima. Hence, in this study, a new evolutionary algorithm, namely, particle swarm optimization is proposed to train the feedforward neural network. This particle swarm optimization feedforward neural network is applied to model the daily rainfall-runoff relationship in Sungai Bedup Basin, Sarawak, Malaysia. The model performance is measured using the coefficient of correlation and the Nash-Sutcliffe coefficient. The input data to the model are current rainfall, antecedent rainfall and antecedent runoff, while the output is current runoff. Particle swarm optimization feedforward neural network simulated the current runoff accurately with R = 0.872 and E2 = 0.775 for the training data set and R = 0.900 and E2 = 0.807 for testing data set. Thus, it can be concluded that the particle swarm optimization feedforward neural network method can be successfully used to model the rainfall-runoff relationship in Bedup Basin and it could be to be applied to other basins. © IRSEN, CEERS, IAU. |
| first_indexed | 2024-03-05T18:40:07Z |
| format | Article |
| id | utm.eprints-26530 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:40:07Z |
| publishDate | 2010 |
| publisher | IJENS Publishers |
| record_format | dspace |
| spelling | utm.eprints-265302018-11-09T08:09:20Z http://eprints.utm.my/26530/ Particle swarm optimization feedforward neural network for modeling runoff Kuok, K. K. Harun, Sobri Shamsuddin, Siti Mariyam TA Engineering (General). Civil engineering (General) The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. However, the backpropagation neural network convergence rate is relatively slow and solutions can be trapped at local minima. Hence, in this study, a new evolutionary algorithm, namely, particle swarm optimization is proposed to train the feedforward neural network. This particle swarm optimization feedforward neural network is applied to model the daily rainfall-runoff relationship in Sungai Bedup Basin, Sarawak, Malaysia. The model performance is measured using the coefficient of correlation and the Nash-Sutcliffe coefficient. The input data to the model are current rainfall, antecedent rainfall and antecedent runoff, while the output is current runoff. Particle swarm optimization feedforward neural network simulated the current runoff accurately with R = 0.872 and E2 = 0.775 for the training data set and R = 0.900 and E2 = 0.807 for testing data set. Thus, it can be concluded that the particle swarm optimization feedforward neural network method can be successfully used to model the rainfall-runoff relationship in Bedup Basin and it could be to be applied to other basins. © IRSEN, CEERS, IAU. IJENS Publishers 2010 Article PeerReviewed Kuok, K. K. and Harun, Sobri and Shamsuddin, Siti Mariyam (2010) Particle swarm optimization feedforward neural network for modeling runoff. International Journal of Environmental Science and Technology, 7 (1). 67 -78. ISSN 2227-2763 http://link.springer.com/article/10.1007/BF03326118 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Kuok, K. K. Harun, Sobri Shamsuddin, Siti Mariyam Particle swarm optimization feedforward neural network for modeling runoff |
| title | Particle swarm optimization feedforward neural network for modeling runoff |
| title_full | Particle swarm optimization feedforward neural network for modeling runoff |
| title_fullStr | Particle swarm optimization feedforward neural network for modeling runoff |
| title_full_unstemmed | Particle swarm optimization feedforward neural network for modeling runoff |
| title_short | Particle swarm optimization feedforward neural network for modeling runoff |
| title_sort | particle swarm optimization feedforward neural network for modeling runoff |
| topic | TA Engineering (General). Civil engineering (General) |
| work_keys_str_mv | AT kuokkk particleswarmoptimizationfeedforwardneuralnetworkformodelingrunoff AT harunsobri particleswarmoptimizationfeedforwardneuralnetworkformodelingrunoff AT shamsuddinsitimariyam particleswarmoptimizationfeedforwardneuralnetworkformodelingrunoff |