Machine Learning-Based Predictive Models for Sustainable Environmental Monitoring and Climate Risk Assessment

Daniela Rus

Machine Learning-Based Predictive Models for Sustainable Environmental Monitoring and Climate Risk Assessment

Author(s)	Daniela Rus
Country	United States
Abstract	Climate change, environmental degradation, biodiversity loss, air and water pollution, and increasing occurrences of extreme weather events have intensified the need for intelligent environmental monitoring and climate risk assessment systems. Traditional environmental monitoring approaches often struggle to process the enormous volume, variety, and velocity of environmental data generated by satellites, remote sensing platforms, Internet of Things (IoT) devices, weather stations, drones, and sensor networks. Recent advances in Machine Learning (ML), Artificial Intelligence (AI), Big Data Analytics, Cloud Computing, Edge Computing, Digital Twin Technology, and Geographical Information Systems (GIS) have enabled the development of predictive environmental models capable of improving sustainability planning, disaster preparedness, ecosystem management, and climate adaptation strategies. This study presents a comprehensive analysis of Machine Learning-based predictive models for sustainable environmental monitoring and climate risk assessment. A qualitative analytical research methodology based on secondary data is employed to investigate supervised and unsupervised learning techniques, deep learning algorithms, remote sensing technologies, environmental sensor networks, intelligent forecasting models, and decision support systems. The research evaluates how machine learning contributes to accurate environmental prediction, climate resilience, biodiversity conservation, pollution control, renewable resource management, and evidence-based environmental policymaking. The findings indicate that machine learning significantly enhances the prediction of air quality, water quality, forest fire occurrence, flood risk, drought severity, landslide susceptibility, agricultural productivity, biodiversity changes, and greenhouse gas emissions. Integrating ML with IoT, satellite imagery, cloud computing, Digital Twins, and edge intelligence enables continuous environmental monitoring, real-time anomaly detection, predictive analytics, and automated early warning systems. These intelligent systems improve resource optimisation, environmental sustainability, and disaster risk reduction while supporting the achievement of the United Nations Sustainable Development Goals (SDGs). Despite these opportunities, challenges remain concerning data quality, model interpretability, climate uncertainty, computational requirements, cybersecurity, privacy, interoperability, and limited environmental datasets in developing regions. Future research should investigate explainable machine learning, federated environmental intelligence, green AI, quantum-enhanced climate modelling, and AI-enabled ecosystem digital twins. The study concludes that machine learning-based predictive models provide a powerful multidisciplinary foundation for sustainable environmental monitoring and climate risk assessment by enabling intelligent, adaptive, transparent, and evidence-based environmental management.
Keywords	Machine Learning, Environmental Monitoring, Climate Risk Assessment, Artificial Intelligence, Sustainability, Remote Sensing, Internet of Things, Predictive Analytics, Climate Change, Digital Twin.
Field	Engineering
Published In	Volume 2, Issue 1, January-February 2020
Published On	2020-01-16

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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-Based Predictive Models for Sustainable Environmental Monitoring and Climate Risk Assessment

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