Modelling hourly runoff using ann for sg. Sarawak Kanan Basin
This study proposes the application of Artificial Neural Network in the modelling hourly runoff for Sungai Sarawak. An Artificial Neural Network is undoubtedly a robust tool for forecasting various non-linear hydrologic processes, including develop a rainfall-runoff model. It is a flexible mathemati...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Year Project Report / IMRAD |
| Language: | en en |
| Published: |
Universiti Malaysia Sarawak (UNIMAS)
2005
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/23079/1/Modelling%20hourly%20runoff%20using%20ann%20for%20Sg.%20Sarawak%20Kanan%20Basin%20%2824%20pgs%29.pdf http://ir.unimas.my/id/eprint/23079/4/Modelling%20hourly%20runoff%20using%20ann%20for%20Sg.%20Sarawak%20Kanan%20Basin%20%28fulltext%29.pdf http://ir.unimas.my/id/eprint/23079/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This study proposes the application of Artificial Neural Network in the modelling hourly runoff for Sungai Sarawak. An Artificial Neural Network is undoubtedly a robust tool for forecasting various non-linear hydrologic processes, including develop a rainfall-runoff model. 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 rainfall-runoff for Buan Bidi 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
model possible. The performances of the ANNs were evaluated based on the coefficient of correlation, R. The back propagation algorithm was adopted for this
study. With the three months of training length data, the optimal model found in this study is the network using five hours of antecedent data, with the combination
of learning rate and the number of neurons in the hidden layer of 0.8 and 150. This model generated the highest R Testing of 0.896 when trained with the scaled
conjugate gradient algorithm (TRAINSCG). It has been found that the ANN has the potential to develop a rainfall-runoff model. After appropriate trainings, they
are able to generate satisfactory results during both of the training and testing phases. |
|---|