Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area
Interest in monitoring severe weather events is cautiously increasing because of the numerous disasters that happen in the recent years in many world countries. Although to predict the trend of precipitation is a difficult task, there are many approaches exist using time series analysis and machine...
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| Format: | Book Chapter |
| Language: | English English |
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Springer
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/19513/6/heavy%20rainfall.png http://umpir.ump.edu.my/id/eprint/19513/11/book20_Heavy%20Rainfall%20Forecasting%20Model%20Using%20Artificial%20Neural%20Network.pdf |
| _version_ | 1796992421627691008 |
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| author | Junaida, Sulaiman Siti Hajar, Wahab |
| author2 | Kim, Kuinam J. |
| author_facet | Kim, Kuinam J. Junaida, Sulaiman Siti Hajar, Wahab |
| author_sort | Junaida, Sulaiman |
| collection | UMP |
| description | Interest in monitoring severe weather events is cautiously increasing because of the numerous disasters that happen in the recent years in many world countries. Although to predict the trend of precipitation is a difficult task, there are many approaches exist using time series analysis and machine learning techniques to provide an alternative way to reduce impact of flood cause by heavy precipitation event. This study applied an Artificial Neural Network (ANN) for prediction of heavy precipitation on monthly basis. For this purpose, precipitation data from 1965 to 2015 from local meteorological stations were collected and used in the study. Different combinations of past precipitation values were produced as forecasting inputs to evaluate the effectiveness of ANN approximation. The performance of the ANN model is compared to statistical technique called Auto Regression Integrated Moving Average (ARIMA). The performance of each approaches is evaluated using root mean square error (RMSE) and correlation coefficient (R 2 ). The results indicate that ANN model is reliable in anticipating above the risky level of heavy precipitation events. |
| first_indexed | 2024-03-06T12:19:51Z |
| format | Book Chapter |
| id | UMPir19513 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T12:19:51Z |
| publishDate | 2018 |
| publisher | Springer |
| record_format | dspace |
| spelling | UMPir195132018-06-06T07:14:19Z http://umpir.ump.edu.my/id/eprint/19513/ Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area Junaida, Sulaiman Siti Hajar, Wahab QA75 Electronic computers. Computer science Interest in monitoring severe weather events is cautiously increasing because of the numerous disasters that happen in the recent years in many world countries. Although to predict the trend of precipitation is a difficult task, there are many approaches exist using time series analysis and machine learning techniques to provide an alternative way to reduce impact of flood cause by heavy precipitation event. This study applied an Artificial Neural Network (ANN) for prediction of heavy precipitation on monthly basis. For this purpose, precipitation data from 1965 to 2015 from local meteorological stations were collected and used in the study. Different combinations of past precipitation values were produced as forecasting inputs to evaluate the effectiveness of ANN approximation. The performance of the ANN model is compared to statistical technique called Auto Regression Integrated Moving Average (ARIMA). The performance of each approaches is evaluated using root mean square error (RMSE) and correlation coefficient (R 2 ). The results indicate that ANN model is reliable in anticipating above the risky level of heavy precipitation events. Springer Kim, Kuinam J. Kim, Hyuncheol Baek, Nakhoon 2018-08-31 Book Chapter PeerReviewed image/png en http://umpir.ump.edu.my/id/eprint/19513/6/heavy%20rainfall.png application/pdf en http://umpir.ump.edu.my/id/eprint/19513/11/book20_Heavy%20Rainfall%20Forecasting%20Model%20Using%20Artificial%20Neural%20Network.pdf Junaida, Sulaiman and Siti Hajar, Wahab (2018) Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area. In: IT Convergence and Security 2017. Lecture Notes in Electrical Engineering, 449 . Springer, Singapore, pp. 68-76. ISBN 978-981-10-6451-7 https://doi.org/10.1007/978-981-10-6451-7_9 |
| spellingShingle | QA75 Electronic computers. Computer science Junaida, Sulaiman Siti Hajar, Wahab Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area |
| title | Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area |
| title_full | Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area |
| title_fullStr | Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area |
| title_full_unstemmed | Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area |
| title_short | Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area |
| title_sort | heavy rainfall forecasting model using artificial neural network for flood prone area |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/19513/6/heavy%20rainfall.png http://umpir.ump.edu.my/id/eprint/19513/11/book20_Heavy%20Rainfall%20Forecasting%20Model%20Using%20Artificial%20Neural%20Network.pdf |
| work_keys_str_mv | AT junaidasulaiman heavyrainfallforecastingmodelusingartificialneuralnetworkforfloodpronearea AT sitihajarwahab heavyrainfallforecastingmodelusingartificialneuralnetworkforfloodpronearea |