Abstract
The frequent occurrence of natural disasters poses significant challenges to human societal security, economic development, and ecosystem stability, making effective forecasting and scientific disaster reduction core topics in geosciences and disaster prevention research. This paper systematically reviews the occurrence mechanisms and evolutionary processes of typical geological disasters such as earthquakes, volcanic eruptions, landslides, and tsunamis, with a particular focus on recent key advances in monitoring technologies, including seismic network deployment, remote sensing, InSAR, GNSS observations, and deep-sea pressure sensors. Furthermore, it explores the application potential of disaster identification and early warning algorithms based on big data integration, machine learning, and artificial intelligence in improving forecasting timeliness and spatial accuracy. Regarding the construction of integrated early warning systems, the importance of multi-hazard and multi-source data monitoring platforms is emphasized, and the establishment of regional-to-global linkage mechanisms is highlighted as crucial for addressing disaster chains and compound disasters. Looking ahead, with the intelligent upgrading of sensor networks and the deep integration of cloud computing and digital twin technologies, natural disaster forecasting and response are expected to become more efficient, collaborative, and intelligent. This study aims to provide theoretical support and technical reference for disaster science research and offer strategic insights for building a globally coordinated disaster prevention and reduction framework.
