Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area

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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Bibliographic Details
Main Authors: Junaida, Sulaiman, Siti Hajar, Wahab
Other Authors: Kim, Kuinam J.
Format: Book Section
Language:English
English
Published: Springer 2018
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/19513/
http://umpir.ump.edu.my/id/eprint/19513/
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
id ump-19513
recordtype eprints
spelling ump-195132018-06-06T07:14:19Z 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 Section 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
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Junaida, Sulaiman
Siti Hajar, Wahab
Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area
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.
author2 Kim, Kuinam J.
author_facet Kim, Kuinam J.
Junaida, Sulaiman
Siti Hajar, Wahab
format Book Section
author Junaida, Sulaiman
Siti Hajar, Wahab
author_sort Junaida, Sulaiman
title 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_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_sort heavy rainfall forecasting model using artificial neural network for flood prone area
publisher Springer
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/19513/
http://umpir.ump.edu.my/id/eprint/19513/
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
first_indexed 2023-09-18T22:27:52Z
last_indexed 2023-09-18T22:27:52Z
_version_ 1777416088101847040

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