Integration of AI-enabled Landslide Early Warning using Multi-Source Geospatial Data Fusion

Thesis Abstract

This study addresses the escalating risk of landslides in geologically diverse and densely populated regions by integrating artificial intelligence with multi-source geospatial data to develop a robust early warning system. The problem stems from limited real-time processing capabilities, data heterogeneity across satellite, aerial, and in-situ sensing platforms, and inadequate transferability of traditional threshold-based warning approaches. The aim is to engineer an AI-enabled landslide early warning framework that fuses satellite imagery, terrain and hydrological data, and social-technical signals to deliver timely probabilistic alerts with quantified uncertainty. Specific objectives include (i) designing a data fusion architecture that harmonizes multisource geospatial inputs at high temporal resolution, (ii) developing and validating machine learning models capable of capturing nonlinear interactions among rainfall, slope stability, soil moisture, vegetation indices, and antecedent triggering conditions, (iii) implementing a real-time processing pipeline using edge-computing and cloud-based components to disseminate warnings to authorities and communities, (iv) evaluating model performance across diverse lithologies and climate regimes, and (v) formulating operational guidelines and governance mechanisms for risk communication and decision-making. Methodologically, the study adopts a mixed-methods design anchored in a data-centric, predictive modeling paradigm. The population comprises landslide-prone catchments within a national-scale hazard portfolio, with a sample of 62 monitoring sites selected to ensure representativeness of slope classes, land cover, and rainfall regimes. Data collection integrates (a) remote sensing products, including Sentinel-2 and Landsat-8 imagery for NDVI and land-surface change detection, Sentinel-1 SAR for soil moisture anomalies and deformation signals, and high-resolution DEM-derived terrain attributes; (b) meteorological observations from an integrated weather station network and satellite-derived precipitation estimates; (c) in-situ sensors providing shallow groundwater levels, pore pressure proxies, and ground vibrations where available; and (d) historical landslide inventories and incident reports for label generation. Data collection instruments include open-access geospatial data portals, calibrated weather sensors, and a dedicated landslide event registry. The validity and reliability of instruments are ensured through cross-validation of satellite products, sensor inter-calibration, and temporal alignment checks. Analytical procedures employ a hierarchical data fusion framework implemented in a Python-based pipeline. At the feature level, engineered variables include rainfall intensity-duration thresholds, antecedent soil moisture, slope and aspect, curvature, lithology indicators, vegetation health indices, and deformation signals. The core predictive models encompass (i) gradient boosting machines (XGBoost) for probabilistic forecasting, (ii) recurrent neural networks (LSTM) to capture temporal dependencies, and (iii) probabilistic graphical models to quantify uncertainty and conditional dependencies among inputs. Model evaluation uses time-series cross-validation, receiving operating characteristic (ROC) analysis, precision-recall metrics, Brier scores, and reliability diagrams. A Bayesian updating mechanism integrates new observations to refine posterior landslide probability estimates. Sensitivity analyses explore the influence of data quality, sensor latency, and spatial misalignment on forecast reliability. The theoretical framing draws on the threshold-acceptance model of disaster warning and the theory of data fusion in geospatial intelligence, complemented by the Cognitive Load Theory to optimize user-interface design for alert dissemination. A conceptual data fusion model summarizes input streams, fusion layers, and decision nodes. Expected findings include improved lead times of warning signals by 24–72 hours under varying rainfall scenarios, higher true-positive rates with controlled false-positive levels, and enhanced spatial precision of predicted landslide footprints through multi-sensor corroboration. The study is anticipated to demonstrate that AI-driven, uncertainty-quantified fusion outperforms single-source or rule-based systems in both accuracy and adaptability across regions with heterogeneous geology and climate. Contributions to knowledge encompass (i) a scalable, generalizable data fusion architecture for AI-enabled landslide forecasting, (ii) empirical evidence on the added value of integrating multi-source geospatial data with temporal models for hazard prediction, (iii) a reproducible methodological framework for uncertainty quantification in early warning, and (iv) operational guidance for governance, risk communication, and decision-making in hazard-prone communities. The main conclusion posits that integrated AI-enabled multi-source data fusion substantially enhances landslide early warning capability, provided that data quality, latency, and user-centered alert design are carefully managed. Recommendations include expanding sensor networks in high-risk zones, establishing standardized data-sharing protocols, and developing region-specific alert thresholds aligned with emergency response capacities to optimize public safety outcomes.