Rapid urbanization and intensifying climate change are placing unprecedented pressure on cities across Asia, including air pollution, urban flooding, heat stress, ecological degradation, infrastructure vulnerability, and increasing environmental health risks. At the same time, recent advances in artificial intelligence (AI), remote sensing, Internet of Things (IoT), geospatial analytics, and urban data science are transforming the way urban environmental systems are monitored, modeled, and managed.
AI-driven approaches are increasingly enabling real-time environmental sensing, predictive analytics, intelligent risk assessment, and adaptive urban governance, providing new opportunities for building smarter, more resilient, and sustainable cities. Emerging technologies such as machine learning, deep learning, digital twins, intelligent sensor networks, and hybrid modeling frameworks are rapidly reshaping environmental monitoring and climate adaptation strategies in urban regions.
This Special Issue aims to provide an interdisciplinary platform for cutting-edge research and practical applications related to AI-enabled urban environmental systems and climate adaptation across Asia. Particular emphasis will be placed on studies integrating environmental science, computational intelligence, data-driven decision-making, and urban sustainability to support next-generation environmental intelligence and resilient city development under climate and urbanization pressures.
This Special Issue welcomes high-quality original research articles, review articles, and perspectives related to, but not limited to, the following topics:
- AI for Urban Environmental Monitoring
- Machine learning for air quality, water quality, and noise monitoring
- Urban heat island analysis and surface temperature modeling
- AI-based environmental risk and exposure assessment
- Intelligent environmental sensing and monitoring systems
- Multi-Source Data Integration and Smart Sensing
- Integration of satellite remote sensing, ground sensors, and IoT networks
- Data fusion techniques for environmental and climate applications
- Low-cost and real-time environmental monitoring systems
- Geospatial analytics and environmental data assimilation
- Machine Learning and Hybrid Modeling
- Physics-informed machine learning for environmental processes
- Hybrid models combining process-based and data-driven approaches
- Spatiotemporal modeling of environmental variables
- Predictive analytics and intelligent environmental forecasting
- Climate Adaptation and Urban Resilience
- AI-based early warning systems for extreme weather events
- Flood prediction and urban drainage modeling
- Heatwave risk assessment and adaptation strategies
- Decision-support tools for urban climate resilience
- Data-Driven Urban Planning and Decision Support
- AI-assisted urban planning and sustainable city design
- Urban digital twins and intelligent infrastructure systems
- Scenario analysis for climate adaptation and mitigation
- Digital tools for environmental management and policy support
- Cross-Regional and Comparative Studies in Asia
- Applications of AI in different climatic and socio-economic contexts across Asia
- Transfer learning and model generalization across regions
- Comparative studies from rapidly urbanizing cities
- Regional environmental intelligence and adaptation strategies
- Emerging Methods and Real-World Applications
- Novel AI algorithms for environmental applications
- Benchmark datasets and model evaluation frameworks
- Explainable AI for environmental systems
- Practical implementations and real-world deployments