Publication:
Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach

Simple item page
dc.citedby3
dc.contributor.authorRufus S.A.en_US
dc.contributor.authorAhmad N.A.en_US
dc.contributor.authorAbdul-Malek Z.en_US
dc.contributor.authorAbdullah N.en_US
dc.contributor.authorid57200580055en_US
dc.contributor.authorid35208076200en_US
dc.contributor.authorid57195728805en_US
dc.contributor.authorid26422769600en_US
dc.date.accessioned2024-10-14T03:21:12Z
dc.date.available2024-10-14T03:21:12Z
dc.date.issued2023
dc.description.abstractThunderstorms 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/APL57308.2023.10182046
dc.identifier.scopus2-s2.0-85166734638
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85166734638&doi=10.1109%2fAPL57308.2023.10182046&partnerID=40&md5=860eba8af5feb5233720ec3c40aa2137
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34624
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleAPL 2023 - 12th Asia-Pacific International Conference on Lightning
dc.subjectLightning
dc.subjectMachine Learning
dc.subjectMeteorological
dc.subjectPerformance Metrics
dc.subjectSMOTE
dc.subjectThunderstorm
dc.subjectThunderstorm Prediction Model
dc.subjectAdaptive boosting
dc.subjectAir traffic control
dc.subjectDecision trees
dc.subjectFlood control
dc.subjectFloods
dc.subjectLearning systems
dc.subjectThunderstorms
dc.subjectWeather forecasting
dc.subjectCG lightning
dc.subjectGradient boosting
dc.subjectHeavy rains
dc.subjectMachine learning approaches
dc.subjectMachine-learning
dc.subjectMeteorological
dc.subjectPerformance metrices
dc.subjectPrediction modelling
dc.subjectSMOTE
dc.subjectThunderstorm prediction model
dc.subjectMachine learning
dc.titleThunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approachen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS