Artificial Intelligence for Environmental Monitoring, Prediction, and Sustainable Ecosystem Management

Environmental systems are becoming increasingly complex under the combined pressures of climate change, biodiversity loss, land-use change, environmental pollution, resource scarcity, and expanding human activities. Addressing these interconnected challenges requires a systems-level understanding of interactions among ecological, hydrological, climatic, technological, and socio-economic processes, along with integrated approaches to evaluate trade-offs and support sustainable management. At the same time, advances in satellite remote sensing, unmanned aerial systems, environmental sensor networks, ecological monitoring programs, and environmental informatics have generated unprecedented volumes of multi-source and multi-scale environmental data.Artificial intelligence (AI), including machine learning, deep learning, foundation models, and geospatial intelligence, provides new opportunities to extract information from these datasets and enhance environmental observation, modeling, forecasting, and management. However, AI's value for environmental systems engineering extends beyond improving prediction accuracy. A key challenge is to integrate data-driven approaches with process-based models, system dynamics, optimization frameworks, and decision-support methods so that complex environmental systems can be analyzed, designed, and managed more effectively.Despite rapid methodological progress, important gaps remain between AI innovation and its practical use in environmental systems analysis and management. Existing studies frequently focus on isolated prediction or classification tasks, while insufficient attention is given to interactions among environmental subsystems, cross-scale processes, feedback mechanisms, uncertainty propagation, and competing management objectives. Challenges also remain in model interpretability, transferability, robustness, and the incorporation of physical and ecological mechanisms into AI-based models. Furthermore, translating model outputs into operational management strategies requires stronger integration of scenario analysis, risk assessment, multi-objective optimization, and decision-making under uncertainty.This Research Topic aims to establish an interdisciplinary platform at the intersection of environmental systems engineering and artificial intelligence. It seeks to bring together researchers from environmental engineering, ecology, geography, hydrology, remote sensing, computer science, operations research, and related disciplines to advance integrated approaches for understanding, predicting, optimizing, and managing complex environmental systems.Particular emphasis will be placed on studies that connect AI-enabled environmental observation and modeling with systems integration, scenario evaluation, optimization, and management decision-making. Relevant contributions may examine how heterogeneous environmental datasets and models can be integrated across spatial, temporal, and organizational scales; how alternative climate, land-use, pollution-control, restoration, or resource-management scenarios can be assessed; how environmental, economic, and societal objectives can be jointly considered through multi-objective optimization; and how AI-supported models can be translated into robust and actionable management strategies.The collection seeks to showcase both methodological innovations and real-world applications that improve environmental monitoring, ecosystem assessment, pollution control, biodiversity conservation, climate adaptation, resource allocation, ecosystem restoration, and sustainable environmental management. By promoting closer integration among data-driven intelligence, process understanding, systems modeling, optimization, and decision science, this Research Topic aims to accelerate the development of reliable, explainable, scalable, and operational solutions for evidence-based environmental management and sustainable development.This Research Topic welcomes high-quality interdisciplinary contributions that advance AI-enabled environmental systems analysis, engineering, and management across terrestrial, freshwater, coastal, marine, agricultural, and urban environments. Topics include, but are not limited to:• Integrated environmental systems modeling combining AI, process-based models, system dynamics, or other mechanistic approaches;• Multi-source and multi-scale environmental data integration, data assimilation, and environmental big-data analytics;• AI-assisted environmental monitoring, remote sensing, sensor networks, and geospatial intelligence;• Scenario analysis for climate change, land-use change, pollution control, ecosystem restoration, and resource management;• Multi-objective and constrained optimization for environmental planning, resource allocation, conservation, and engineering design;• Decision-making under uncertainty, environmental risk assessment, adaptive management, and robust decision-support systems;• Coupled human–natural and socio-environmental systems modeling;• Environmental digital twins and intelligent environmental management platforms;• Physics-informed, ecology-informed, and knowledge-guided machine learning;• Explainable, interpretable, transferable, and uncertainty-aware AI for environmental systems;• Ecological modeling, ecosystem forecasting, biodiversity conservation, and ecosystem resilience;• Pollution assessment, source identification, ecological risk analysis, and pollution-control optimization;• Climate adaptation, mitigation, and resilience planning;• Smart agriculture, forestry, water-resource management, and sustainable land management;• Ecosystem-service assessment and optimization of trade-offs among ecological, economic, and social objectives;• AI-enabled early-warning, planning, and environmental management decision-support systems.This Research Topic is part of a series developed in collaboration with the 1st International Conference on Artificial Intelligence for Ecology and Environment. Explore the companion topics in this series: Artificial Intelligence for Ecological Discovery, Biodiversity, and Ecosystem Dynamics, Frontiers in Ecology and EvolutionArtificial Intelligence for Forest Monitoring, Resilience, and Sustainable Management under Global Change, Frontiers in Forest and Global Change Topic editors: Jie Gao, Jinran Wu, Jing Xu, Xiaoyong Qian Source: https://www.frontiersin.org/research-topics/85366/artificial-intelligence-for-environmental-monitoring-prediction-and-sustainable-ecosystem-management