Geological Hazard Prediction and Prevention: A Review of Mechanisms, Monitoring, and Mitigation

As industrialization expands, modern infrastructure increasingly collides with volatile geological environments, driving a sharp escalation of complex, cascading geohazards. These destructive hazard chains represent a critical global threat to structural and economic resilience. Despite this escalating risk, current early warning systems predominantly rely on fragmented, static methodologies that fail to capture the dynamic reality of temporal hazard evolution. Furthermore, contemporary predictive models are bifurcated between deterministic physical models demanding exhaustive geotechnical parameters and data-driven artificial intelligence algorithms functioning as opaque black boxes devoid of physical interpretability. To resolve these limitations, this review systematically evaluates the modern geohazard landscape through a coupled active-passive conceptual model. This framework systematically evaluates how primary high-energy failures trigger subsequent instability in surrounding geomaterials. The synthesis reveals that integrating space-air-ground multi-scale monitoring including orbital InSAR, UAV photogrammetry, and distributed ground sensors establishes a vital surveillance continuum for early hazard identification. Because these heterogeneous data streams possess significant environmental noise, rigorous multi-source data fusion remains essential to minimize false alarms and resolve spatial discontinuities. Analytically, physically based models provide indispensable mechanical transparency by explicitly simulating material deformation, whereas data-driven ensemble algorithms excel at processing high-dimensional, nonlinear geospatial inputs. Ultimately, transitioning toward proactive, real-time risk reduction demands the integration of physics-informed neural networks (PINNs) that embed geomechanical constraints into computational pipelines. Coupling these hybrid architectures with interactive digital twin technologies will transform static hazard mapping into dynamic virtual environments, empowering engineers to successfully mitigate evolving disaster chains globally.

Geological hazards; Disaster risk reduction; Real-time monitoring; Data-driven forecasting; Early warning systems; Physics-informed neural networks (PINNs).

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