Big Data Analytics and Artificial Intelligence for Decision Support in Urban Ecology and Environmental Management

Abstract

Abstract: The volume, complexity, and integration of information that is available from remote sensing platforms, internet of things (IoT) sensors and devices, geographic information systems (GIS), environmental monitoring stations, and social data streams are growing, and these sources of data are increasingly driving the need for intelligent decision support systems to be used in urban ecology and environmental management. In this paper, a comprehensive review and conceptual framework are addressed to combine the use of Big Data Analytics and Artificial Intelligence (AI) in the context of sustainable environmental planning and ecosystem management in urban areas. The proposed framework involves multi-source data collection, preprocessing, feature engineering, scalable storage of large volumes of data, and AI powered predictive analytics through machine learning, deep learning, graph neural networks, and reinforcement learning. The framework allows to make decisions in real time and based on data that enable an accurate environmental monitoring, prediction of pollution, optimization of waste management, assessment of water resources, and carbon emission analysis. In addition, the study covers the use of cloud-edge computing architectures, spatial analytics and intelligent visualization for enhancing the operation and policy making efficiency. The current challenges and research opportunities such as data heterogeneity, scalability, privacy and security, and the interpretability of models are critically examined and new research avenues such as explainable AI, digital twins, and federated learning are explored. The proposed framework illustrates how the fusion of the Big Data Analytics and AI technologies can greatly support the ecological resilience, environmental sustainability and evidence-based decision making in smart cities of the future.