Radial basis function modeling of hourly streamflow hydrograph
An artificial neural network is well known as a flexible mathematical tool that has the ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. The radial basis function (RBF) method is applied to model the relationship between rainfall and runoff for Sungai Be...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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American Society of Civil Engineers
2007
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| Online Access: | http://eprints.utm.my/8487/1/8487.pdf |
| _version_ | 1796854391051911168 |
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| author | Nor, Nor Irwan Ahmat Harun, Sobri Mohd. Kassim, Amir Hashim |
| author_facet | Nor, Nor Irwan Ahmat Harun, Sobri Mohd. Kassim, Amir Hashim |
| author_sort | Nor, Nor Irwan Ahmat |
| collection | ePrints |
| description | An artificial neural network is well known as a flexible mathematical tool that has the ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. The radial basis function (RBF) method is applied to model the relationship between rainfall and runoff for Sungai Bekok Catchment (Johor, Malaysia) and Sungai Ketil catchment (Kedah, Malaysia). The RBF is used to predict the streamflow hydrograph based on storm events. Evaluation on the performance of RBF is demonstrated based on errors (between predicted and actual) and comparison with the results of the Hydrologic Engineering Center hydrologic modeling system model. It is obvious that the RBF method offers an accurate modeling of streamflow hydrograph. |
| first_indexed | 2024-03-05T18:13:36Z |
| format | Article |
| id | utm.eprints-8487 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:13:36Z |
| publishDate | 2007 |
| publisher | American Society of Civil Engineers |
| record_format | dspace |
| spelling | utm.eprints-84872017-10-19T00:07:31Z http://eprints.utm.my/8487/ Radial basis function modeling of hourly streamflow hydrograph Nor, Nor Irwan Ahmat Harun, Sobri Mohd. Kassim, Amir Hashim TA Engineering (General). Civil engineering (General) An artificial neural network is well known as a flexible mathematical tool that has the ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. The radial basis function (RBF) method is applied to model the relationship between rainfall and runoff for Sungai Bekok Catchment (Johor, Malaysia) and Sungai Ketil catchment (Kedah, Malaysia). The RBF is used to predict the streamflow hydrograph based on storm events. Evaluation on the performance of RBF is demonstrated based on errors (between predicted and actual) and comparison with the results of the Hydrologic Engineering Center hydrologic modeling system model. It is obvious that the RBF method offers an accurate modeling of streamflow hydrograph. American Society of Civil Engineers 2007 Article PeerReviewed application/pdf en http://eprints.utm.my/8487/1/8487.pdf Nor, Nor Irwan Ahmat and Harun, Sobri and Mohd. Kassim, Amir Hashim (2007) Radial basis function modeling of hourly streamflow hydrograph. Journal Of Hydrologic Engineering, 12 (1). pp. 113-123. ISSN 1084-0699 http://dx.doi.org/10.1061/(ASCE)1084-0699(2007)12:1(113) 10.1061/(ASCE)1084-0699(2007)12:1(113) |
| spellingShingle | TA Engineering (General). Civil engineering (General) Nor, Nor Irwan Ahmat Harun, Sobri Mohd. Kassim, Amir Hashim Radial basis function modeling of hourly streamflow hydrograph |
| title | Radial basis function modeling of hourly streamflow hydrograph |
| title_full | Radial basis function modeling of hourly streamflow hydrograph |
| title_fullStr | Radial basis function modeling of hourly streamflow hydrograph |
| title_full_unstemmed | Radial basis function modeling of hourly streamflow hydrograph |
| title_short | Radial basis function modeling of hourly streamflow hydrograph |
| title_sort | radial basis function modeling of hourly streamflow hydrograph |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utm.my/8487/1/8487.pdf |
| work_keys_str_mv | AT nornorirwanahmat radialbasisfunctionmodelingofhourlystreamflowhydrograph AT harunsobri radialbasisfunctionmodelingofhourlystreamflowhydrograph AT mohdkassimamirhashim radialbasisfunctionmodelingofhourlystreamflowhydrograph |