Advances in weather and climate extreme studies: a systematic comparative review

Abstract

Abstract There is a growing evidence-based concerns pointing to global increase of weather and climate extreme events (WCEE). What decides whether these hazards become a disaster is how the society and humanity prepare and responds to it. Although various researchers have step up efforts through scholarly contributions in response to WCEE. However, the task of utilizing the needed tools, selecting the most suitable approach, and choosing appropriate techniques for WCEE studies can be daunting for researchers. Moreover, the technicality and complexity associated with the wide array of models and methods present additional challenges when it comes to their practical implementationF in disaster applications. This review comprehensively explores four approaches in WCEE studies: statistical analysis, geospatial modeling, MCDA, and AI-based techniques. It evaluates their effectiveness in assessing impacts within WCEE and highlights AI’s (ML and DL’s) superior performance compared to other conventional methods in disaster-related studies.

Keywords

• Weather and climate extremes
• Disaster risk reduction
• Approaches
• Methods
• Machine learning
• Deep learning