ARTIFICIAL NEURAL NETWORK FOR WATER LEVEL PREDICTION IN A RIVER UNDER TIDAL INFLUENCE
This study proposes the application of Artificial Neural Network in the prediction of water level under tidal influence for Sungai Sarawak. An Artificial Neural Network is undoubtedly a robust tool for forecasting various non-linear hydrologic processes, including the water level prediction. I...
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| Main Author: | |
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| Format: | Final Year Project Report / IMRAD |
| Language: | en |
| Published: |
Universiti Malaysia Sarawak, (UNIMAS)
2004
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/49332/1/Maliana%20ft.pdf http://ir.unimas.my/id/eprint/49332/ |
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| Summary: | This study proposes the application of Artificial Neural Network in the
prediction of water level under tidal influence for Sungai Sarawak. 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 Siniawan Station. Distinctive networks were trained and tested
using hourly data obtained from the DID Department in Kuching. Various training
parameters were considered in order to gain the best prediction possible. The
performances of the ANNs were evaluated based on the coefficient of efficiency, E` and
the coefficient of correlation, R. The back propagation algorithm was adopted for this
study. The optimal model found in this study is the network using two hours of
antecedent data, with the combination of learning rate and the number of neurons in the
hidden layer of 0.8 and 40. This model generated the highest R Testing of 0.9425 when
trained with the scaled conjugate gradient algorithm (trainscg). 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 the limitations of the ANNs had
been discussed, based on the results attained in this study. |
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