[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-124694-105":59,"doc-detail-124694-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","thunderstorm-prediction-model-using-smote-sampling-and-machine-learning-approach","Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach","","Thunderstorms cause severe impacts through lightning and heavy rainfall, leading to fatalities, floods, and crop damage across many regions. This study predicts thunderstorm occurrence using historical lightning and meteorological observations from 2011–2018 in Peninsular Malaysia’s southern areas, where class imbalance is driven by the rarity of positive CG lightning and the complex nonlinear behavior of thunderstorms. SMOTE resampling is applied to balance training data, followed by evaluation with five machine learning models. Results show strong performance, and the SMOTE with Gradient Boosting model provides the best overall metrics for this region, supporting early alerting and planning by relevant authorities.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/thunderstorm-prediction-model-using-smote-sampling-and-machine-learning-approach/124694/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/thunderstorm-prediction-model-using-smote-sampling-and-machine-learning-approach/124694.png","ImageObject",300,407,{"name":92,"@type":93},"Finn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-29","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address in thunderstorm prediction?","Question",{"text":113,"@type":114},"The study targets the difficulty of predicting thunderstorms accurately due to the dynamic, nonlinear, and complex nature of thunderstorms and lightning, along with dataset imbalance.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is class imbalance handled in the proposed method?",{"text":118,"@type":114},"SMOTE is introduced as a resampling technique to overcome the imbalance in the training dataset caused by differences such as the rarity of positive CG lightning.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning algorithms are evaluated and what model performs best?",{"text":122,"@type":114},"Decision Trees, AdaBoost, Random Forest, Extra Trees, and Gradient Boosting are tested. The SMOTE and Gradient Boosting model yields the best performance based on reported metrics.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},124694,1785893959,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":81},549768064778,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach  \nShirley Anak Rufus Department of Electrical and Electronics Engineering, Universiti Malaysia Sarawak Sarawak, Malaysia [rshirley@unimas.my](rshirley@unimas.my)[ ](rshirley@unimas.my)Institute of High Voltage and High Current (IVAT), Faculty of Electrical Engineering, Universiti Teknologi Malaysia Johor, Malaysia [ashirley@graduate.utm.my](ashirley@graduate.utm.my)  \nN.A. Ahmad  \nInstitute of High Voltage and High Current (IVAT), Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor, Malaysia [noorazlinda@utm.my](noorazlinda@utm.my)  \nZ. Abdul-Malek Institute of High Voltage and High Current (IVAT), Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor, Malaysia [zulkurnain@utm.my](zulkurnain@utm.my)  \nNoradlina Abdullah Lightning and Earthing Unit TNB Research Sdn. Bhd. Selangor, Malaysia [noradlina.abdullah@tnb.com](noradlina.abdullah@tnb.com). my  \nAbstract—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.  \nKeywords—Thunderstorm, Lightning, Machine Learning, SMOTE, Thunderstorm Prediction Model, Meteorological, Performance Metrics  \nI. INTRODUCTION  \nA thunderstorm is caused by a cumulonimbus cloud that produces the electric discharge. Typically, the thunderstorm is associated with lightning and accompanied by heavy rainfall and wind. Thunderstorms adversely impact humans, industries, infrastructure, and other related sectors that directly cause human injuries, fatalities, and financial losses. An estimated 24 thousand fatalities and 240 thousand injuries annually are attributable to lightning [1] . Malaysia has an average of 204 days of thunderstorms which is equivalent to 40 strikes per kilometre per year [2] . In Malaysia, a total of 132 deaths over ten years from 2008 until August 2019 led toa very high lightning fatalities rate, TD = 167 [1] . About RM 250 million losses in infrastructure damages and business disruption due to power outages caused by lightning each year [2] . Lightning occurrences recorded by the Lightning  \nDetection Networks System (LDNS) operated by Tenaga Nasional Berhad-Research (TNBR) and meteorological data obtained from weather stations owned by the Department of Meteorological Malaysia (known as MetMalaysia) have millions of recorded data. A combination of both datasets formed big data which is useful in the process of developing a prediction model to analyse the pat","cbCaisPtcgqQZ8Ux","https://ap.wps.com/l/cbCaisPtcgqQZ8Ux","pdf",165821,"English","# Introduction\n## Thunderstorm background and risks\n## Data sources and big-data motivation\n## Challenges in thunderstorm prediction\n## Prior machine learning approaches","[{\"question\":\"What problem does the study address in thunderstorm prediction?\",\"answer\":\"The study targets the difficulty of predicting thunderstorms accurately due to the dynamic, nonlinear, and complex nature of thunderstorms and lightning, along with dataset imbalance.\"},{\"question\":\"How is class imbalance handled in the proposed method?\",\"answer\":\"SMOTE is introduced as a resampling technique to overcome the imbalance in the training dataset caused by differences such as the rarity of positive CG lightning.\"},{\"question\":\"Which machine learning algorithms are evaluated and what model performs best?\",\"answer\":\"Decision Trees, AdaBoost, Random Forest, Extra Trees, and Gradient Boosting are tested. The SMOTE and Gradient Boosting model yields the best performance based on reported metrics.\"}]","Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach | PDF"]