Cloud-Enabled AI Analytics for Urban Green Space Optimization: Enhancing Microclimate Benefits in High-Density Urban Areas

This research proposes a comprehensive framework for cloud-enabled AI analytics applied to urban green space optimization in high-density environments. The study addresses the critical challenge of microclimate management in densely populated urban areas through the integration of IoT sensor networks, cloud computing architecture, and machine learning algorithms. Environmental monitoring systems capture high-resolution spatiotemporal data across multiple parameters including temperature gradients, humidity profiles, air quality indicators, and pedestrian thermal comfort metrics. The cloud-based infrastructure enables efficient data aggregation, storage, and processing capabilities while supporting complex analytical functions through distributed computing resources. Implementation of machine learning algorithms including random forest, gradient boosting, and CNN-LSTM hybrids facilitates pattern recognition in microclimate data, achieving accuracy rates exceeding 92% in selected validation scenarios. Multi-objective optimization techniques identify Pareto-optimal green infrastructure configurations balancing thermal performance, implementation costs, and maintenance requirements. Evaluation across global case studies demonstrates temperature reductions of 2.7-6.2°C in pedestrian zones, 18-42% decreases in building energy consumption, and significant improvements in stormwater management capacity. The developed path-finding algorithms enhance pedestrian routing by prioritizing thermal comfort without compromising practical distance constraints. This framework presents a scalable approach for evidence-based green space planning, contributing to enhanced urban resilience and sustainable development in densely populated metropolitan areas.