Hourly water level prediction of sungai bedup catchement using pre-developed ANNs model from Siniawan catchment
This study proposes the application of Artificial Neural Network in the prediction of water level under tidal influence for Sadong Basin. An Artificial Neural Network is undoubtedly a robust tool for forecasting various non-linear hydrologic processes, including the water level prediction. It is...
| Main Author: | |
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| Format: | Final Year Project Report / IMRAD |
| Language: | English English |
| Published: |
Universiti Malaysia Sarawak (UNIMAS)
2009
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| Online Access: | http://ir.unimas.my/id/eprint/7492/ http://ir.unimas.my/id/eprint/7492/1/HOURLY%20WATER%20LEVEL%20PREDICTION%20OF%20SUNGAI%20BEDUP%20%2824%20pages%29.pdf http://ir.unimas.my/id/eprint/7492/8/Calvin%20HCC%20%20ft.pdf |
| _version_ | 1848836144101326848 |
|---|---|
| author | Hong,, Calvin Chiao Chun |
| author_facet | Hong,, Calvin Chiao Chun |
| author_sort | Hong,, Calvin Chiao Chun |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | This study proposes the application of Artificial Neural Network in the
prediction of water level under tidal influence for Sadong Basin. An Artificial Neural
Network is undoubtedly a robust tool for forecasting various non-linear hydrologic
processes, including the water level prediction. It is a flexible mathematical structure
which is capable to generalize patterns in imprecise or noisy and ambiguous input and
output data sets. In this study, the ANNs were developed specifically to forecast the
hourly water level for Sg. Bedup Station. Distinctive networks were trained and tested
using hourly data obtained from the DID Department in Kuching. The performances of
the ANNs were evaluated based on the coefficient of efficiency, E2 and the coefficient of
correlation, R. The back propagation algorithm was adopted for this study. The models
used in this study is the network trained with scaled conjugate gradient algorithm
(trainscg) with two hours of antecedent data, learning rate and the number of neurons in
the hidden layer of 0.8 and 40 respectively. In this study, the models generated the R
value for testing of 1.00 when it trained. It has been found that the ANN has the
potential to solve the problems of water level prediction. After appropriate trainings,
they are able to generate satisfactory results during both of the training and testing
phases. Further, the strength and limitations of ANNs had been discussed, based on the
resulted obtained in this study. |
| first_indexed | 2025-11-15T06:19:05Z |
| format | Final Year Project Report / IMRAD |
| id | unimas-7492 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T06:19:05Z |
| publishDate | 2009 |
| publisher | Universiti Malaysia Sarawak (UNIMAS) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-74922023-11-15T08:48:56Z http://ir.unimas.my/id/eprint/7492/ Hourly water level prediction of sungai bedup catchement using pre-developed ANNs model from Siniawan catchment Hong,, Calvin Chiao Chun TA Engineering (General). Civil engineering (General) TC Hydraulic engineering. Ocean engineering This study proposes the application of Artificial Neural Network in the prediction of water level under tidal influence for Sadong Basin. An Artificial Neural Network is undoubtedly a robust tool for forecasting various non-linear hydrologic processes, including the water level prediction. It is a flexible mathematical structure which is capable to generalize patterns in imprecise or noisy and ambiguous input and output data sets. In this study, the ANNs were developed specifically to forecast the hourly water level for Sg. Bedup Station. Distinctive networks were trained and tested using hourly data obtained from the DID Department in Kuching. The performances of the ANNs were evaluated based on the coefficient of efficiency, E2 and the coefficient of correlation, R. The back propagation algorithm was adopted for this study. The models used in this study is the network trained with scaled conjugate gradient algorithm (trainscg) with two hours of antecedent data, learning rate and the number of neurons in the hidden layer of 0.8 and 40 respectively. In this study, the models generated the R value for testing of 1.00 when it trained. It has been found that the ANN has the potential to solve the problems of water level prediction. After appropriate trainings, they are able to generate satisfactory results during both of the training and testing phases. Further, the strength and limitations of ANNs had been discussed, based on the resulted obtained in this study. Universiti Malaysia Sarawak (UNIMAS) 2009 Final Year Project Report / IMRAD NonPeerReviewed text en http://ir.unimas.my/id/eprint/7492/1/HOURLY%20WATER%20LEVEL%20PREDICTION%20OF%20SUNGAI%20BEDUP%20%2824%20pages%29.pdf text en http://ir.unimas.my/id/eprint/7492/8/Calvin%20HCC%20%20ft.pdf Hong,, Calvin Chiao Chun (2009) Hourly water level prediction of sungai bedup catchement using pre-developed ANNs model from Siniawan catchment. [Final Year Project Report / IMRAD] (Unpublished) |
| spellingShingle | TA Engineering (General). Civil engineering (General) TC Hydraulic engineering. Ocean engineering Hong,, Calvin Chiao Chun Hourly water level prediction of sungai bedup catchement using pre-developed ANNs model from Siniawan catchment |
| title | Hourly water level prediction of sungai bedup catchement using pre-developed ANNs model from Siniawan catchment |
| title_full | Hourly water level prediction of sungai bedup catchement using pre-developed ANNs model from Siniawan catchment |
| title_fullStr | Hourly water level prediction of sungai bedup catchement using pre-developed ANNs model from Siniawan catchment |
| title_full_unstemmed | Hourly water level prediction of sungai bedup catchement using pre-developed ANNs model from Siniawan catchment |
| title_short | Hourly water level prediction of sungai bedup catchement using pre-developed ANNs model from Siniawan catchment |
| title_sort | hourly water level prediction of sungai bedup catchement using pre-developed anns model from siniawan catchment |
| topic | TA Engineering (General). Civil engineering (General) TC Hydraulic engineering. Ocean engineering |
| url | http://ir.unimas.my/id/eprint/7492/ http://ir.unimas.my/id/eprint/7492/1/HOURLY%20WATER%20LEVEL%20PREDICTION%20OF%20SUNGAI%20BEDUP%20%2824%20pages%29.pdf http://ir.unimas.my/id/eprint/7492/8/Calvin%20HCC%20%20ft.pdf |