Neural network for rainfall runoff modelling
Neural network is a very useful data modelling tool that is able to capture and represent complex input and output relationships. The advantage of neural network lies in its ability to represent both linear and non-linear relationships and also its ability to learn these relationships directly from...
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| Format: | Final Year Project Report / IMRAD |
| Language: | English |
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University Malaysia Sarawak, UNIMAS.
2004
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| Online Access: | http://ir.unimas.my/id/eprint/2896/ http://ir.unimas.my/id/eprint/2896/8/HAFIZ%20FADILLAH%20ALHADI.pdf |
| _version_ | 1848835095593484288 |
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| author | Hafiz Fadillah, Alhadi |
| author_facet | Hafiz Fadillah, Alhadi |
| author_sort | Hafiz Fadillah, Alhadi |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Neural network is a very useful data modelling tool that is able to capture and represent complex input and output relationships. The advantage of neural network lies in its ability to represent both linear and non-linear relationships and also its ability to learn these relationships directly from the data being modelled. So, the purpose of this study is to develop a rainfall runoff model for Sungai Tinjar with outlet at Long Jegan, The network was trained using Back Propagation Algorithm. |
| first_indexed | 2025-11-15T06:02:25Z |
| format | Final Year Project Report / IMRAD |
| id | unimas-2896 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:02:25Z |
| publishDate | 2004 |
| publisher | University Malaysia Sarawak, UNIMAS. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-28962023-11-23T08:56:29Z http://ir.unimas.my/id/eprint/2896/ Neural network for rainfall runoff modelling Hafiz Fadillah, Alhadi Q Science (General) T Technology (General) TA Engineering (General). Civil engineering (General) Neural network is a very useful data modelling tool that is able to capture and represent complex input and output relationships. The advantage of neural network lies in its ability to represent both linear and non-linear relationships and also its ability to learn these relationships directly from the data being modelled. So, the purpose of this study is to develop a rainfall runoff model for Sungai Tinjar with outlet at Long Jegan, The network was trained using Back Propagation Algorithm. University Malaysia Sarawak, UNIMAS. 2004 Final Year Project Report / IMRAD NonPeerReviewed text en http://ir.unimas.my/id/eprint/2896/8/HAFIZ%20FADILLAH%20ALHADI.pdf Hafiz Fadillah, Alhadi (2004) Neural network for rainfall runoff modelling. [Final Year Project Report / IMRAD] (Unpublished) |
| spellingShingle | Q Science (General) T Technology (General) TA Engineering (General). Civil engineering (General) Hafiz Fadillah, Alhadi Neural network for rainfall runoff modelling |
| title | Neural network for rainfall runoff modelling |
| title_full | Neural network for rainfall runoff modelling |
| title_fullStr | Neural network for rainfall runoff modelling |
| title_full_unstemmed | Neural network for rainfall runoff modelling |
| title_short | Neural network for rainfall runoff modelling |
| title_sort | neural network for rainfall runoff modelling |
| topic | Q Science (General) T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://ir.unimas.my/id/eprint/2896/ http://ir.unimas.my/id/eprint/2896/8/HAFIZ%20FADILLAH%20ALHADI.pdf |