부산대학교 도서관

Extreme weather events like floods, heavy rains, and droughts have become more constant and intensive as a result of climatic change. There are three machine learning (ML) models correlated in this study: Random Forest (RF), Convolutional Neural Networks (CNNs), and regression methods (such as Support Vector Regression and Decision Trees). The ability of these models to predict extreme weather occurrences, simulate climate conditions, and analyze air quality in urban environments is assessed. Although all the models described in this paper performed well, their strengths, weaknesses, and applicability to climatology and other types of simulations are also analyzed in addition to the exceptional gaps that need further research to decide which model is more suitable for predicting extreme meteorological phenomena. (Abstract)