DATA-DRIVEN CLIMATE INFORMATICS: A SYSTEMATIC REVIEW OF INTEGRATING MACHINE LEARNING AND GEOSPATIAL ANALYTICS FOR SUSTAINABLE ENVIRONMENTAL MANAGEMENT

Abstract

The escalating impacts of climate change necessitate advanced methodologies for environmental monitoring and resource management. This review synthesizes recent advancements in climate informatics, focusing on the integration of machine learning (ML), geospatial analytics, and big data systems. By analyzing multi-source datasets from satellites and IoT sensors, the study explores how predictive analytics can forecast ecological risks and enhance climate modeling. A significant emphasis is placed on the transition from physics-based models to data-driven frameworks that leverage "Digital Twin" concepts for regional resilience. Furthermore, the research investigates the role of Explainable AI (XAI) in providing transparency for high-stakes, evidence-based sustainability policies. The synthesis identifies critical research gaps, including baseline selection in deep learning and the need for energy-aware "Green AI" strategies. Ultimately, this work provides a comprehensive roadmap for utilizing data science as a catalyst for global environmental stewardship and proactive climate resilience.