Thunderstorm Prediction Model Using SMOTE Sampling and Machine Learning Approach

• 主要な著者: Rufus S.A., Ahmad N.A., Abdul-Malek Z., Abdullah N.
• その他の著者: 57200580055
• フォーマット: Conference Paper
• 出版事項: Institute of Electrical and Electronics Engineers Inc. 2024
• 主題: 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

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...