Machine Learning Techniques for Climate Change Prediction and Environmental Sustainability

Takeo Kanade

Machine Learning Techniques for Climate Change Prediction and Environmental Sustainability

Author(s)	Takeo Kanade
Country	United States
Abstract	Climate change has become one of the most significant global challenges, affecting ecosystems, biodiversity, agriculture, water resources, public health, and socioeconomic development. The increasing availability of environmental data from satellites, Internet of Things (IoT) devices, remote sensing, weather stations, Geographic Information Systems (GIS), and Earth observation platforms has accelerated the application of Machine Learning (ML), Artificial Intelligence (AI), Deep Learning (DL), Big Data Analytics, Cloud Computing, Edge Computing, Digital Twin Technology, High-Performance Computing (HPC), Explainable Artificial Intelligence (XAI), and Predictive Analytics for climate modelling and environmental sustainability. These technologies enable accurate climate forecasting, disaster prediction, biodiversity monitoring, carbon emission analysis, renewable energy optimisation, and evidence-based environmental policymaking. This study presents a comprehensive analysis of Machine Learning Techniques for Climate Change Prediction and Environmental Sustainability. A qualitative analytical research methodology based on secondary data is adopted to examine machine learning algorithms, environmental monitoring systems, climate prediction models, sustainability applications, implementation challenges, and future research directions. The study investigates how AI-driven predictive models enhance climate forecasting, environmental risk assessment, ecosystem management, pollution monitoring, renewable energy planning, and climate adaptation strategies. The findings indicate that machine learning significantly improves weather forecasting, drought prediction, flood modelling, wildfire detection, carbon emission estimation, biodiversity conservation, and renewable energy management. Deep learning algorithms analyse large-scale satellite imagery for land-use classification and environmental monitoring, while explainable AI improves transparency in climate-related decision-making. Digital twins simulate environmental systems for sustainable planning, and IoT-enabled sensor networks provide continuous real-time environmental monitoring. Despite these opportunities, challenges remain concerning climate data uncertainty, computational complexity, data quality, model interpretability, infrastructure limitations, cybersecurity, ethical AI governance, and interdisciplinary collaboration. Future research should focus on AI-powered climate digital twins, federated environmental learning, quantum machine learning, autonomous environmental monitoring, explainable climate models, and sustainable AI frameworks for global climate resilience. The study concludes that machine learning provides a transformative foundation for climate change prediction and environmental sustainability by integrating intelligent analytics, digital technologies, and multidisciplinary scientific research to support resilient ecosystems, informed policymaking, and sustainable development.
Keywords	Machine Learning, Climate Change, Environmental Sustainability, Artificial Intelligence, Deep Learning, Remote Sensing, Predictive Analytics, Environmental Monitoring, Climate Prediction, Smart Environment.
Field	Engineering
Published In	Volume 2, Issue 6, November-December 2020
Published On	2020-12-26

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Machine Learning Techniques for Climate Change Prediction and Environmental Sustainability

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