AI-Driven Eco-Engineering Framework for Climate-Resilient Urban Systems Using Real-Time Social and Environmental Data

Abstract

Compound climate risks, including flooding, heatwaves, and environmental degradation, are more likely to affect urban areas, and available resilience strategies are still disconnected, with an ecological, technological, and social focus. This paper presents a proposal of an AI-driven eco-engineering control that combines real-time environmental sensing (remote sensing, meteorological, hydrological, and IoT data) with social sensing (geotagged social media data) to aid in adaptive and data-driven urban climate decision-making. This framework is based on the multi-layered architecture comprising of data acquisition, preprocessing, integration of multi-modal features, AI-based prediction (LSTM and SVM), spatial risk mapping, and optimization of nature-based solutions on eco-engineering basis.

It is theoretical and methodological in character and offers a comprehensive framework but not an entire and implemented case study empirically. The proposed system will be found to increase the predictive accuracy, better spatial identification of hotspots of climate risk, and allow real-time and context-aware decision support, in contrast to traditional single-source systems. The framework provides a scalable way to achieve proactive, resilient, and sustainable urban planning in the face of climate change by connecting AI-based analytics with ecological engineering interventions.