RAINFALL-RUNOFF MODEL FOR SUNGAI BEDUP BASIN USING ARTIFICIAL NEURAL NETWORK
A relatively new tool, artificial neural network (ANN), was applied to simulate the runoff at a river section. A catchment, Sungai Bedup basin of Samarahan division, Sarawak with a drainage area of about 47.77 km2 was considered in the study. The rainfall and river flow data of 2 years (1995 a...
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| Format: | Final Year Project Report / IMRAD |
| Language: | en en en |
| Published: |
Universiti Malaysia Sarawak, (UNIMAS)
2003
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/51376/1/MAH%20YAU%20SENG%20dsva.pdf http://ir.unimas.my/id/eprint/51376/2/MAH%20YAU%20SENG%2024%20pgs.pdf http://ir.unimas.my/id/eprint/51376/3/MAH%20YAU%20SENG%20ft.pdf http://ir.unimas.my/id/eprint/51376/ |
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| Summary: | A relatively new tool, artificial neural network (ANN), was applied to
simulate the runoff at a river section. A catchment, Sungai Bedup basin of
Samarahan division, Sarawak with a drainage area of about 47.77 km2 was
considered in the study. The rainfall and river flow data of 2 years (1995 and
1998) were used as the input and target to train the neural networks. The trained
model was used to predict the flow of the year 1999. Different combinations of
variables and parameters have been investigated to find the best forecasting
model.
Results from both training and testing simulation showed a high
degree of agreement between the simulated and the measured river flow. A good
fit, regression analysis coefficient, R2, over 0.9 and 0.8 were obtained from the
training and testing simulations, respectively. |
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