Machine Learning In Climate Impact Assessment: Bridging Data And Policy

Abstract

Climate change creates problems that have never been seen before in ecosystems, economies and the lives of people. Being able to quickly and accurately evaluate its results matters a lot for making good decisions in policymaking. With the help of recent machine learning (ML), scientists can now predict future climate scenarios and spot unusual events in complex multidimensional data. This research reviews the ways machine learning methods are now used in climate science to improve how assessments of impacts are carried out. In addition, it assesses the way policymakers bring ML-based insights into their decision making. We discuss the difference between various machine learning (ML) models used with climate data, explain their real impact and address questions about model explanations, data availability and applying findings to public policies. According to the findings, ML can help to unite climate data analysis and making informed choices.