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Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach

Thunderstorms are one of the most destructive phenomena worldwide and are primarily associated with lightning and heavy rain that cause human fatalities, urban floods, and crop damage. Therefore, predicting thunderstorms with reasonable accuracy is one of the crucial requirements for the planning an...

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Main Authors: Rufus S.A., Ahmad N.A., Abdul-Malek Z., Abdullah N.
Other Authors: 57200580055
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Subjects:
Lightning
Machine Learning
Meteorological
Performance Metrics
SMOTE
Thunderstorm
Thunderstorm Prediction Model
Adaptive boosting
Air traffic control
Decision trees
Flood control
Floods
Learning systems
Thunderstorms
Weather forecasting
CG lightning
Gradient boosting
Heavy rains
Machine learning approaches
Machine-learning
Performance metrices
Prediction modelling
Thunderstorm prediction model
Machine learning
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id my.uniten.dspace-34624
record_format dspace
spelling my.uniten.dspace-346242024-10-14T11:21:12Z Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach Rufus S.A. Ahmad N.A. Abdul-Malek Z. Abdullah N. 57200580055 35208076200 57195728805 26422769600 Lightning Machine Learning Meteorological Performance Metrics SMOTE Thunderstorm Thunderstorm Prediction Model Adaptive boosting Air traffic control Decision trees Flood control Floods Learning systems Thunderstorms Weather forecasting CG lightning Gradient boosting Heavy rains Machine learning approaches Machine-learning Meteorological Performance metrices Prediction modelling SMOTE Thunderstorm prediction model Machine learning Thunderstorms are one of the most destructive phenomena worldwide and are primarily associated with lightning and heavy rain that cause human fatalities, urban floods, and crop damage. Therefore, predicting thunderstorms with reasonable accuracy is one of the crucial requirements for the planning and management of many applications, including agriculture, flood control, and air traffic control. This study extensively applied the historical lightning and meteorological data from 2011 to 2018 of the southern regions of Peninsular Malaysia to predict thunderstorm occurrence. Positive CG lightning rarely occurs compared to negative CG lightning and also due to the non-linear and complex characteristics of the thunderstorm and lightning itself, leading to an imbalance in the dataset. The resampling technique called SMOTE is introduced to overcome the imbalance of the training dataset. Then the dataset is trained and tested with 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 a good prediction with accuracy (74% to 95%), recall (72% to 93%), precision (76% to 97%), and F1-Score (74% to 95%) with SMOTE. The SMOTE and GB model prediction model is the best algorithm for thunderstorm prediction for this region in terms of performance metrics. In the future, the prediction results based on the lightning pattern and weather dataset will likely alert the related authorities to make an early strategy to handle the occurrence of thunderstorms. � 2023 IEEE. Final 2024-10-14T03:21:12Z 2024-10-14T03:21:12Z 2023 Conference Paper 10.1109/APL57308.2023.10182046 2-s2.0-85166734638 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166734638&doi=10.1109%2fAPL57308.2023.10182046&partnerID=40&md5=860eba8af5feb5233720ec3c40aa2137 https://irepository.uniten.edu.my/handle/123456789/34624 All Open Access Green Open Access Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Lightning
Machine Learning
Meteorological
Performance Metrics
SMOTE
Thunderstorm
Thunderstorm Prediction Model
Adaptive boosting
Air traffic control
Decision trees
Flood control
Floods
Learning systems
Thunderstorms
Weather forecasting
CG lightning
Gradient boosting
Heavy rains
Machine learning approaches
Machine-learning
Meteorological
Performance metrices
Prediction modelling
SMOTE
Thunderstorm prediction model
Machine learning
spellingShingle Lightning
Machine Learning
Meteorological
Performance Metrics
SMOTE
Thunderstorm
Thunderstorm Prediction Model
Adaptive boosting
Air traffic control
Decision trees
Flood control
Floods
Learning systems
Thunderstorms
Weather forecasting
CG lightning
Gradient boosting
Heavy rains
Machine learning approaches
Machine-learning
Meteorological
Performance metrices
Prediction modelling
SMOTE
Thunderstorm prediction model
Machine learning
Rufus S.A.
Ahmad N.A.
Abdul-Malek Z.
Abdullah N.
Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach
description Thunderstorms are one of the most destructive phenomena worldwide and are primarily associated with lightning and heavy rain that cause human fatalities, urban floods, and crop damage. Therefore, predicting thunderstorms with reasonable accuracy is one of the crucial requirements for the planning and management of many applications, including agriculture, flood control, and air traffic control. This study extensively applied the historical lightning and meteorological data from 2011 to 2018 of the southern regions of Peninsular Malaysia to predict thunderstorm occurrence. Positive CG lightning rarely occurs compared to negative CG lightning and also due to the non-linear and complex characteristics of the thunderstorm and lightning itself, leading to an imbalance in the dataset. The resampling technique called SMOTE is introduced to overcome the imbalance of the training dataset. Then the dataset is trained and tested with 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 a good prediction with accuracy (74% to 95%), recall (72% to 93%), precision (76% to 97%), and F1-Score (74% to 95%) with SMOTE. The SMOTE and GB model prediction model is the best algorithm for thunderstorm prediction for this region in terms of performance metrics. In the future, the prediction results based on the lightning pattern and weather dataset will likely alert the related authorities to make an early strategy to handle the occurrence of thunderstorms. � 2023 IEEE.
author2 57200580055
author_facet 57200580055
Rufus S.A.
Ahmad N.A.
Abdul-Malek Z.
Abdullah N.
format Conference Paper
author Rufus S.A.
Ahmad N.A.
Abdul-Malek Z.
Abdullah N.
author_sort Rufus S.A.
title Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach
title_short Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach
title_full Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach
title_fullStr Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach
title_full_unstemmed Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach
title_sort thunderstorm prediction model using smote sampling and machine learning approach
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
_version_ 1814060106040475648
score 13.214268

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