Artificial Intelligence Models for Environmental Decision Support

Ana Deletic

Artificial Intelligence Models for Environmental Decision Support

Author(s)	Prof. Ana Deletic
Country	Australia
Abstract	Environmental decision-making increasingly depends on large, diverse, and continuously changing datasets generated through satellites, sensors, geographic information systems, climate observations, environmental surveys, and monitoring networks. Conventional analytical approaches may face difficulties when environmental systems involve nonlinear relationships, uncertainty, spatial variability, and rapidly changing conditions. Artificial intelligence provides advanced computational approaches for processing environmental information, identifying patterns, forecasting risks, optimizing resources, and supporting evidence-based decisions. This study examines artificial intelligence models for environmental decision support through a conceptual and descriptive research approach. The study focuses on machine learning, deep learning, natural language processing, reinforcement learning, computer vision, predictive analytics, explainable artificial intelligence, and hybrid AI models. Applications include climate-risk assessment, air-quality prediction, water-resource management, biodiversity monitoring, disaster-risk management, pollution detection, land-use planning, energy management, and environmental impact assessment. An integrated framework is developed connecting environmental data acquisition, preprocessing, AI modeling, uncertainty assessment, decision support, stakeholder interpretation, and adaptive environmental management. The analysis indicates that AI models can improve environmental forecasting, anomaly detection, spatial analysis, scenario evaluation, and resource-allocation decisions. Nevertheless, their implementation involves challenges related to data quality, model transparency, uncertainty, computational requirements, cybersecurity, bias, interoperability, technical expertise, and environmental costs associated with computing infrastructure. The study emphasizes that AI should function as decision support rather than an isolated replacement for environmental expertise, institutional governance, or stakeholder participation. Future research should investigate explainable AI, multimodal environmental models, digital twins, edge AI, causal machine learning, AI-enabled climate adaptation, autonomous environmental monitoring, and human-centered decision-support systems. The proposed framework provides a structured foundation for integrating artificial intelligence with environmental science and governance to support more informed, adaptive, transparent, and sustainable environmental decision-making.
Keywords	Artificial Intelligence, Environmental Decision Support, Machine Learning, Deep Learning, Environmental Management, Predictive Analytics, Climate Risk, Explainable AI, Environmental Monitoring, Sustainable Decision-Making
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-17

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American International Journal of Advanced Research and Development

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Artificial Intelligence Models for Environmental Decision Support

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