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Smart Environmental Decision Models Using Artificial Intelligence

Author(s) Iadine Chades
Country Australia
Abstract Increasing environmental complexity, climate-related hazards, biodiversity loss, pollution, and resource pressures have created a growing need for intelligent decision-support mechanisms. Conventional environmental decision-making may be constrained by fragmented information, delayed monitoring, uncertainty, and the difficulty of interpreting large and heterogeneous environmental datasets. Artificial intelligence (AI) offers new opportunities to develop smart environmental decision models capable of integrating real-time observations, historical datasets, spatial information, predictive analytics, and environmental indicators. This study examines AI-based environmental decision models with particular emphasis on environmental monitoring, risk prediction, resource allocation, ecological assessment, climate adaptation, pollution management, and sustainable environmental planning. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The study analyzes how machine learning, deep learning, natural language processing, computer vision, remote sensing, geographic information systems, and intelligent optimization can support environmental decision processes.
AI-enabled models can identify environmental patterns, predict potential hazards, classify ecological conditions, and support alternative management scenarios. Integration with IoT sensors and satellite observations can further enable near-real-time environmental intelligence. However, implementation challenges include data quality, model uncertainty, interpretability, computational requirements, cybersecurity, institutional capacity, and the risk of overdependence on automated recommendations. The study emphasizes human-centered AI frameworks in which algorithmic outputs complement scientific expertise and institutional decision-making. Future research should investigate explainable AI, environmental digital twins, multimodal environmental intelligence, adaptive decision models, autonomous monitoring, and uncertainty-aware environmental decision support. AI-enabled environmental decision models can contribute to more timely, evidence-based, and adaptive environmental management when supported by reliable data, transparent analytical procedures, and appropriate human oversight.
Keywords Artificial Intelligence, Environmental Decision Models, Environmental Management, Machine Learning, Environmental Risk, Predictive Analytics, Smart Decision Support, Remote Sensing, GIS, Sustainability.
Field Engineering
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-10
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