Early prediction system using neural network in Kelantan River, Malaysia
Flood is a major disaster that happens around the world. It has caused the loss of many precious lives and destruction of large amounts of property. The possibility of flood can be determined depends on many factors that consist of rainfall, water flow rate and water level. This project aims to desi...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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IEEE
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/59484/ http://psasir.upm.edu.my/id/eprint/59484/1/Early%20prediction%20system%20using%20neural%20network%20in%20Kelantan%20River%2C%20Malaysia.pdf |
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| author | Anuar, Mohd Azrol Syafiee Abdul Rahman, Ribhan Zafira Mohd Noor, Samsul Bahari Che Soh, Azura Zulkafli, Zed Diyana |
| author_facet | Anuar, Mohd Azrol Syafiee Abdul Rahman, Ribhan Zafira Mohd Noor, Samsul Bahari Che Soh, Azura Zulkafli, Zed Diyana |
| author_sort | Anuar, Mohd Azrol Syafiee |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Flood is a major disaster that happens around the world. It has caused the loss of many precious lives and destruction of large amounts of property. The possibility of flood can be determined depends on many factors that consist of rainfall, water flow rate and water level. This project aims to design a water level prediction system which is used to analyze the Kelantan River water level based on Sokor River, Galas River and Lebir River flow rate and rainfall of at Ldg. Kuala Nal and Ldg. Kenneth. The system utilizes neural networks in predicting the water level for 5 hours ahead. This system has 5 inputs and 1 output prediction. This prediction system focusses on comparing the conventional method and the Neural Network Autoregressive with Exogenous Input (NNARX) system in determining the possibility of flood. The result shows that the NNARX can predict the water level of Kelantan River much more better compared to conventional method. The performance of the system is based on the value of the means square error (MSE). The MSE of the conventional method is 0.2550 meanwhile for NNARX is 1.342 × 10−4. |
| first_indexed | 2025-11-15T11:01:52Z |
| format | Conference or Workshop Item |
| id | upm-59484 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:01:52Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-594842018-03-07T03:12:56Z http://psasir.upm.edu.my/id/eprint/59484/ Early prediction system using neural network in Kelantan River, Malaysia Anuar, Mohd Azrol Syafiee Abdul Rahman, Ribhan Zafira Mohd Noor, Samsul Bahari Che Soh, Azura Zulkafli, Zed Diyana Flood is a major disaster that happens around the world. It has caused the loss of many precious lives and destruction of large amounts of property. The possibility of flood can be determined depends on many factors that consist of rainfall, water flow rate and water level. This project aims to design a water level prediction system which is used to analyze the Kelantan River water level based on Sokor River, Galas River and Lebir River flow rate and rainfall of at Ldg. Kuala Nal and Ldg. Kenneth. The system utilizes neural networks in predicting the water level for 5 hours ahead. This system has 5 inputs and 1 output prediction. This prediction system focusses on comparing the conventional method and the Neural Network Autoregressive with Exogenous Input (NNARX) system in determining the possibility of flood. The result shows that the NNARX can predict the water level of Kelantan River much more better compared to conventional method. The performance of the system is based on the value of the means square error (MSE). The MSE of the conventional method is 0.2550 meanwhile for NNARX is 1.342 × 10−4. IEEE 2017 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/59484/1/Early%20prediction%20system%20using%20neural%20network%20in%20Kelantan%20River%2C%20Malaysia.pdf Anuar, Mohd Azrol Syafiee and Abdul Rahman, Ribhan Zafira and Mohd Noor, Samsul Bahari and Che Soh, Azura and Zulkafli, Zed Diyana (2017) Early prediction system using neural network in Kelantan River, Malaysia. In: 2017 IEEE 15th Student Conference on Research and Development (SCOReD), 13-14 Dec. 2017, Putrajaya, Malaysia. (pp. 104-109). 10.1109/SCORED.2017.8305412 |
| spellingShingle | Anuar, Mohd Azrol Syafiee Abdul Rahman, Ribhan Zafira Mohd Noor, Samsul Bahari Che Soh, Azura Zulkafli, Zed Diyana Early prediction system using neural network in Kelantan River, Malaysia |
| title | Early prediction system using neural network in Kelantan River, Malaysia |
| title_full | Early prediction system using neural network in Kelantan River, Malaysia |
| title_fullStr | Early prediction system using neural network in Kelantan River, Malaysia |
| title_full_unstemmed | Early prediction system using neural network in Kelantan River, Malaysia |
| title_short | Early prediction system using neural network in Kelantan River, Malaysia |
| title_sort | early prediction system using neural network in kelantan river, malaysia |
| url | http://psasir.upm.edu.my/id/eprint/59484/ http://psasir.upm.edu.my/id/eprint/59484/ http://psasir.upm.edu.my/id/eprint/59484/1/Early%20prediction%20system%20using%20neural%20network%20in%20Kelantan%20River%2C%20Malaysia.pdf |