Space-Ground Hybrid AI System for Flood and Landslide Monitoring Using Multi-Level Data Fusion

Abstract

Floods and landslides represent critical hydrometeorological hazards whose frequency and intensity are amplified by extreme precipitation, deforestation, unplanned urban expansion, and inadequate land-use management. Conventional Early Warning Systems (EWS) are typically constrained by reliance on single-source data streams such as satellite observations, IoT sensor networks, or community reports, resulting in fragmented situational awareness, elevated predictive uncertainty, and limited lead time. To address these limitations, this study proposes a Space-Ground Hybrid AI framework that integrates heterogeneous data sources through a hierarchical multi-level fusion architecture. The proposed system combines convolutional neural networks (CNNs) for spatial feature extraction, long short-term memory (LSTM) networks for temporal sequence modeling, and gradient-boosted ensembles for hazard-specific prediction, enabling comprehensive modeling of both large-scale environmental patterns and localized dynamic processes. These outputs are systematically fused at feature, decision, and risk levels to derive a Unified Hazard Risk Index (UHRI), which provides an interpretable and operationally actionable representation of multi-hazard risk. Experimental evaluation using multi-spectral satellite imagery, high-frequency IoT sensor measurements, and simulated community intelligence demonstrates that the proposed hybrid model achieves superior performance, with an accuracy of 91%, an F1-score of 0.89, and a substantial reduction in false alarm rates compared to singlesource baselines. Furthermore, the framework improves warning lead time and maintains robust performance under partial data degradation scenarios, highlighting its resilience and suitability for real-world deployment. Overall, the results validate the effectiveness of multi-modal data fusion and hybrid AI modeling in enhancing predictive reliability, spatial coverage, and temporal responsiveness for disaster early warning systems, providing a scalable foundation for next-generation risk monitoring and management solutions.