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Flood prediction using artificial neural networks: A case study in Temerloh, Pahang

Flood is one of the natural disasters that causes damage to properties, and sometimes loss of lives. Floods in Malaysia happen every year, especially in East Coast Peninsular Malaysia, due to the Northeast Monsoon and climate change, which may lead to heavy rainfall throughout the end of the year. T...

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Main Authors: Ahmad Jazli, Abdul Rahman, Nor Azuana, Ramli
Format: Article
Language:English
Published: Qeios Ltd. 2024
Subjects:
GE Environmental Sciences
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/41053/1/TUZ29Y.pdf
http://umpir.ump.edu.my/id/eprint/41053/
https://doi.org/10.32388/TUZ29Y
https://doi.org/10.32388/TUZ29Y
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spelling my.ump.umpir.410532024-04-24T04:30:02Z http://umpir.ump.edu.my/id/eprint/41053/ Flood prediction using artificial neural networks: A case study in Temerloh, Pahang Ahmad Jazli, Abdul Rahman Nor Azuana, Ramli GE Environmental Sciences QA76 Computer software Flood is one of the natural disasters that causes damage to properties, and sometimes loss of lives. Floods in Malaysia happen every year, especially in East Coast Peninsular Malaysia, due to the Northeast Monsoon and climate change, which may lead to heavy rainfall throughout the end of the year. Temerloh is one of the districts in Pahang that frequently encounters flood events, especially between November and January every year. Even though there are multiple efforts in flood mitigation and preparation, the damage to citizens and properties every year has cost thousands of Ringgits and the time taken to clean the damages caused by floods. Despite this, research on flood prediction in the state needs to be done using machine learning techniques. Due to this, this research explored the hydrological and meteorological factors that caused the flood in Temerloh and developed a machine-learning model capable of predicting the flood occurrence. The study used a dataset from the National Hydrological Network Management System (SPRHiN), which consists of hydrological data, and weather underground for the meteorological data in the location. The correlation analysis found that stream flow and water level are highly correlated to floods, with correlation coefficients (r values) of 0.83 and 0.76, respectively, while the temperature is inversely related to floods with a -0.28 correlation value. A lower temperature has a higher chance of rain and subsequent flooding. The results show that the model, by using an artificial neural network (ANN), has produced an accuracy of 0.9909 and a good performance of the area under the receiver operating characteristic curve (ROC) curve (AUC) at 0.888. The model also shows a low error with the mean squared error (MSE) of 0.009 and the root-mean-squared error (RMSE) of 0.096. The R2 value of 0.768 and F1 value of 0.875 indicate that the model has high precision and recall. Afterwards, a flood monitoring dashboard was created to visualize the data interactively. This research is vital in understanding the flood factors in Pahang and would offer academic insight for future research in floods. In addition, the flood monitoring dashboard will significantly assist governments and authorities in focusing the flood management efforts in areas at high risk of flood and be used to aid the state's future development. Qeios Ltd. 2024-02-28 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41053/1/TUZ29Y.pdf Ahmad Jazli, Abdul Rahman and Nor Azuana, Ramli (2024) Flood prediction using artificial neural networks: A case study in Temerloh, Pahang. Qeios (TUZ29Y). pp. 1-13. ISSN 2632-3834. (Published) https://doi.org/10.32388/TUZ29Y https://doi.org/10.32388/TUZ29Y
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic GE Environmental Sciences
QA76 Computer software
spellingShingle GE Environmental Sciences
QA76 Computer software
Ahmad Jazli, Abdul Rahman
Nor Azuana, Ramli
Flood prediction using artificial neural networks: A case study in Temerloh, Pahang
description Flood is one of the natural disasters that causes damage to properties, and sometimes loss of lives. Floods in Malaysia happen every year, especially in East Coast Peninsular Malaysia, due to the Northeast Monsoon and climate change, which may lead to heavy rainfall throughout the end of the year. Temerloh is one of the districts in Pahang that frequently encounters flood events, especially between November and January every year. Even though there are multiple efforts in flood mitigation and preparation, the damage to citizens and properties every year has cost thousands of Ringgits and the time taken to clean the damages caused by floods. Despite this, research on flood prediction in the state needs to be done using machine learning techniques. Due to this, this research explored the hydrological and meteorological factors that caused the flood in Temerloh and developed a machine-learning model capable of predicting the flood occurrence. The study used a dataset from the National Hydrological Network Management System (SPRHiN), which consists of hydrological data, and weather underground for the meteorological data in the location. The correlation analysis found that stream flow and water level are highly correlated to floods, with correlation coefficients (r values) of 0.83 and 0.76, respectively, while the temperature is inversely related to floods with a -0.28 correlation value. A lower temperature has a higher chance of rain and subsequent flooding. The results show that the model, by using an artificial neural network (ANN), has produced an accuracy of 0.9909 and a good performance of the area under the receiver operating characteristic curve (ROC) curve (AUC) at 0.888. The model also shows a low error with the mean squared error (MSE) of 0.009 and the root-mean-squared error (RMSE) of 0.096. The R2 value of 0.768 and F1 value of 0.875 indicate that the model has high precision and recall. Afterwards, a flood monitoring dashboard was created to visualize the data interactively. This research is vital in understanding the flood factors in Pahang and would offer academic insight for future research in floods. In addition, the flood monitoring dashboard will significantly assist governments and authorities in focusing the flood management efforts in areas at high risk of flood and be used to aid the state's future development.
format Article
author Ahmad Jazli, Abdul Rahman
Nor Azuana, Ramli
author_facet Ahmad Jazli, Abdul Rahman
Nor Azuana, Ramli
author_sort Ahmad Jazli, Abdul Rahman
title Flood prediction using artificial neural networks: A case study in Temerloh, Pahang
title_short Flood prediction using artificial neural networks: A case study in Temerloh, Pahang
title_full Flood prediction using artificial neural networks: A case study in Temerloh, Pahang
title_fullStr Flood prediction using artificial neural networks: A case study in Temerloh, Pahang
title_full_unstemmed Flood prediction using artificial neural networks: A case study in Temerloh, Pahang
title_sort flood prediction using artificial neural networks: a case study in temerloh, pahang
publisher Qeios Ltd.
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41053/1/TUZ29Y.pdf
http://umpir.ump.edu.my/id/eprint/41053/
https://doi.org/10.32388/TUZ29Y
https://doi.org/10.32388/TUZ29Y
_version_ 1822924290399404032
score 13.235362

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