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A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia

Thunderstorms are one of the most destructive natural phenomena on the planet, as they are predominantly associated with lightning and heavy rainfall that result in human deaths, urban flooding, and agricultural damage. Thus, accurate thunderstorm prediction is essential for planning and managing ag...

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Main Authors: Shirley, Rufus, Noor Azlinda, Ahmad, Zulkurnain, Abdul Malek, Noradlina, Abdullah
Format: Proceeding
Language:English
Published: 2023
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://ir.unimas.my/id/eprint/43752/3/A%20Comparative%20Analysis.pdf
http://ir.unimas.my/id/eprint/43752/
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spelling my.unimas.ir.437522023-12-18T06:13:06Z http://ir.unimas.my/id/eprint/43752/ A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia Shirley, Rufus Noor Azlinda, Ahmad Zulkurnain, Abdul Malek Noradlina, Abdullah TK Electrical engineering. Electronics Nuclear engineering Thunderstorms are one of the most destructive natural phenomena on the planet, as they are predominantly associated with lightning and heavy rainfall that result in human deaths, urban flooding, and agricultural damage. Thus, accurate thunderstorm prediction is essential for planning and managing agriculture, flood control, and air traffic control. This study utilized historical lightning and meteorological data from 2011 to 2018 in the southern regions of Peninsular Malaysia to predict thunderstorm occurrences. The lightning dataset is classified into three class ranges, where the high range of lightning rarely occurs in this region compared to the low and medium ranges of lightning because of the non-linear and complex characteristics of the thunderstorm and lightning itself, leading to an imbalanced dataset. The k-fold and stratified cross-validation (CV) methods and a resampling technique called SMOTE are introduced to overcome the imbalance in the training dataset. Then the dataset is trained and tested using five Machine Learning (ML) algorithms, including Decision Trees (DT), Adaptive Boosting (AdaBoost), Random Forest (RF), Extra Trees (ET), and Gradient Boosting (GB). The results have shown that the GB ML model using stratified k-fold CV and SMOTE is the best algorithm for thunderstorm prediction for this region, with accuracy ranging from 74% to 95%, recall ranging from 72% to 93%, precision ranging from 76% to 97%, and F1-Score ranging from 74% to 95%. Future thunderstorm predictions based on lightning patterns and meteorological datasets are expected to establish an early strategy to address the presence of thunderstorms by notifying the relevant authorities, to prevent any damage that may be caused by the thunderstorms. 2023-12-17 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/43752/3/A%20Comparative%20Analysis.pdf Shirley, Rufus and Noor Azlinda, Ahmad and Zulkurnain, Abdul Malek and Noradlina, Abdullah (2023) A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia. In: 2023 International Symposium on Lightning Protection (XVII SIPDA), Suzhou, China, 9-13 October 2023, Suzhou, China.
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Shirley, Rufus
Noor Azlinda, Ahmad
Zulkurnain, Abdul Malek
Noradlina, Abdullah
A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia
description Thunderstorms are one of the most destructive natural phenomena on the planet, as they are predominantly associated with lightning and heavy rainfall that result in human deaths, urban flooding, and agricultural damage. Thus, accurate thunderstorm prediction is essential for planning and managing agriculture, flood control, and air traffic control. This study utilized historical lightning and meteorological data from 2011 to 2018 in the southern regions of Peninsular Malaysia to predict thunderstorm occurrences. The lightning dataset is classified into three class ranges, where the high range of lightning rarely occurs in this region compared to the low and medium ranges of lightning because of the non-linear and complex characteristics of the thunderstorm and lightning itself, leading to an imbalanced dataset. The k-fold and stratified cross-validation (CV) methods and a resampling technique called SMOTE are introduced to overcome the imbalance in the training dataset. Then the dataset is trained and tested using five Machine Learning (ML) algorithms, including Decision Trees (DT), Adaptive Boosting (AdaBoost), Random Forest (RF), Extra Trees (ET), and Gradient Boosting (GB). The results have shown that the GB ML model using stratified k-fold CV and SMOTE is the best algorithm for thunderstorm prediction for this region, with accuracy ranging from 74% to 95%, recall ranging from 72% to 93%, precision ranging from 76% to 97%, and F1-Score ranging from 74% to 95%. Future thunderstorm predictions based on lightning patterns and meteorological datasets are expected to establish an early strategy to address the presence of thunderstorms by notifying the relevant authorities, to prevent any damage that may be caused by the thunderstorms.
format Proceeding
author Shirley, Rufus
Noor Azlinda, Ahmad
Zulkurnain, Abdul Malek
Noradlina, Abdullah
author_facet Shirley, Rufus
Noor Azlinda, Ahmad
Zulkurnain, Abdul Malek
Noradlina, Abdullah
author_sort Shirley, Rufus
title A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia
title_short A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia
title_full A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia
title_fullStr A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia
title_full_unstemmed A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia
title_sort comparative analysis using machine learning approach for thunderstorm prediction in southern region of peninsular malaysia
publishDate 2023
url http://ir.unimas.my/id/eprint/43752/3/A%20Comparative%20Analysis.pdf
http://ir.unimas.my/id/eprint/43752/
_version_ 1787140536945082368
score 13.211869

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