Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network
This study proposes the application of Artificial Neural Network (ANN)in predicting missing precipitation to predicting daily water level for Sg. Bedup station located in Batang Sadong Basin, Sarawak.ANN is undoubtedly a strong tool for forecasting various non- linear hydrologic proc...
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| Format: | Final Year Project Report / IMRAD |
| Language: | English English |
| Published: |
UNIMAS
2006
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/7088/ http://ir.unimas.my/id/eprint/7088/1/Estimating%20missing%20precipitation%20to%20optimize%20parameters...%2824%20pgs%29.pdf http://ir.unimas.my/id/eprint/7088/4/Dayang%20Suhaila%20Awang%20Suhaili%20ft.pdf |
| _version_ | 1848836054784671744 |
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| author | Dayang Suhaila, Awang Suhaili |
| author_facet | Dayang Suhaila, Awang Suhaili |
| author_sort | Dayang Suhaila, Awang Suhaili |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | This study proposes the application of Artificial Neural Network (ANN)in predicting missing precipitation to
predicting daily water level for Sg. Bedup station located in Batang Sadong Basin, Sarawak.ANN is undoubtedly a strong tool for forecasting various non-
linear hydrologic processes, including the missing
precipitation and water level prediction. ANN was chosen based on its ability to extract the relation between the inputs and outputs of a process without the
physics known explicitly.In this study, the ANN was
developed specifically to predict the daily missing precipitation and data simulated are utilized to optimize
prediction accuracy for daily water level. Typical
networks were trained and tested using daily data obtained from the Drainage and Irrigation Department
(DID) Kota Samarahan.Various training parameters were considered in order to gain the best prediction possible. The performances of the ANN were evaluated based on the coefficient of correlation, R. The back propagation algorithm was adopted for this study. The optimal model for predicting missing data
found in this study is the network with the combination of learning rate and the number of neurons in the hidden layer of 0.2 and 60. This model generated the highest coefficient of correlation value of 0.964 when trained with the The Resilient Back
propagation(trainrp). It has been found that the ANN has the potential to solve the problems of estimation missing precipitatio in predicting daily water level. After
appropriate trainings, they are able to generate satisfactory results during both of the training and testing phases. |
| first_indexed | 2025-11-15T06:17:40Z |
| format | Final Year Project Report / IMRAD |
| id | unimas-7088 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T06:17:40Z |
| publishDate | 2006 |
| publisher | UNIMAS |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-70882023-07-10T02:28:30Z http://ir.unimas.my/id/eprint/7088/ Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network Dayang Suhaila, Awang Suhaili TA Engineering (General). Civil engineering (General) This study proposes the application of Artificial Neural Network (ANN)in predicting missing precipitation to predicting daily water level for Sg. Bedup station located in Batang Sadong Basin, Sarawak.ANN is undoubtedly a strong tool for forecasting various non- linear hydrologic processes, including the missing precipitation and water level prediction. ANN was chosen based on its ability to extract the relation between the inputs and outputs of a process without the physics known explicitly.In this study, the ANN was developed specifically to predict the daily missing precipitation and data simulated are utilized to optimize prediction accuracy for daily water level. Typical networks were trained and tested using daily data obtained from the Drainage and Irrigation Department (DID) Kota Samarahan.Various training parameters were considered in order to gain the best prediction possible. The performances of the ANN were evaluated based on the coefficient of correlation, R. The back propagation algorithm was adopted for this study. The optimal model for predicting missing data found in this study is the network with the combination of learning rate and the number of neurons in the hidden layer of 0.2 and 60. This model generated the highest coefficient of correlation value of 0.964 when trained with the The Resilient Back propagation(trainrp). It has been found that the ANN has the potential to solve the problems of estimation missing precipitatio in predicting daily water level. After appropriate trainings, they are able to generate satisfactory results during both of the training and testing phases. UNIMAS 2006 Final Year Project Report / IMRAD NonPeerReviewed text en http://ir.unimas.my/id/eprint/7088/1/Estimating%20missing%20precipitation%20to%20optimize%20parameters...%2824%20pgs%29.pdf text en http://ir.unimas.my/id/eprint/7088/4/Dayang%20Suhaila%20Awang%20Suhaili%20ft.pdf Dayang Suhaila, Awang Suhaili (2006) Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network. [Final Year Project Report / IMRAD] (Unpublished) |
| spellingShingle | TA Engineering (General). Civil engineering (General) Dayang Suhaila, Awang Suhaili Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network |
| title | Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network |
| title_full | Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network |
| title_fullStr | Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network |
| title_full_unstemmed | Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network |
| title_short | Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network |
| title_sort | estimating missing precipitation to optimize parameters for prediction of daily water level using artificial neural network |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://ir.unimas.my/id/eprint/7088/ http://ir.unimas.my/id/eprint/7088/1/Estimating%20missing%20precipitation%20to%20optimize%20parameters...%2824%20pgs%29.pdf http://ir.unimas.my/id/eprint/7088/4/Dayang%20Suhaila%20Awang%20Suhaili%20ft.pdf |