Most machine learning (ML) studies on predicting co-seismic landslides have relied on Peak Ground Acceleration (PGA). The PGA of the ground strongly correlates with the relative position and azimuth of the seismogenic faults. Using the co-seismic landslide records of the 2008 Wenchuan earthquake, we show that the ML model using the distances and azimuths from the epicenter to sites performs better than the PGA model regarding accuracy and actual prediction results. The distances and azimuths are more accessible than the PGA because obtaining accurate and realistic large-scale PGAs is difficult. Considering their computational efficiency and cost-effectiveness, the ML models utilizing distances and azimuths from the epicenter to the sites as inputs could be an alternative to PGA-based models for evaluating the impact of co-seismic landslides. Notably, these models prove advantageous in near-real-time scenarios and settings requiring high spatial resolution, and provide favorable assistance in achieving the goal of sustainable development of society.

Earthquakes occurring in the Taiwan region have the potential to cause significant damage and loss of life. To ensure an effective emergence response and mitigate the impact of seismic events, it is imperative to develop an earthquake fatality prediction model that incorporates the Taiwan region’s seismic background, geological context, and socioeconomic level. This research article analyzes 35 historically damaging earthquakes in the Taiwan region, considering their fatality records, macroseismic intensities (modified Mercalli shaking intensity scale), and population exposure, to propose a specific seismic fatality ratio applicable to the Taiwan region. The model incorporates adjustments based on the gross national income per capita to address temporal and spatial disparities resulting from socioeconomic development. The predicted results highlight the superior performance of this fatality estimation model compared to other existing models. Furthermore, the earthquake fatality estimation model for the Taiwan region differs significantly from the models employed for South China and Qinghai–Tibet, suggesting variations in geological background, earthquake-resistant structures, and seismic resilience of society between the Taiwan region and mainland China. Moreover, a comparison with the U.S. Geological Survey model for the Taiwan region reveals that previous evaluations of the Taiwan region earthquakes greatly underestimated the number of fatalities.

Earthquakes in marine settings may trigger long chains of secondary hazards. These chains propagate from the seafloor to coastal infrastructure and on to society, yet they have rarely been quantified as a whole. To address this gap, this study proposes a quantitative model of marine seismic hazard chains that combines a catastrophe-dynamics formulation with directed-graph analysis. This network draws on documented secondary hazards from dozens of major tsunamis and includes 39 nodes. This study characterises its topology through degree centrality, network density, centralisation, mean path length, and a layer-resolved net-flow measure. The result is a single-source, dual-hub, multi-sink cascade that is shallow, with most hazards reachable within about two steps. Submarine and coastal earthquakes drive the whole chain, while tsunamis and critical infrastructure failure act as the two high-throughput hubs that amplify and route the flow. Critical network failure is the principal convergence node, where these losses accumulate. The model reveals a monotonic cascade from geological triggers to socioeconomic losses, in which the engineering layer is the gateway through which natural hazards convert into technological failures. A comparison with a mainland earthquake chain at a common taxonomic resolution shows that the two systems share the same four-layer architecture. The mainland earthquake chain exhibits stronger centralisation while the marine tsunami chain presents a more distributed structure. These findings suggest that the controllable engineering hubs, rather than the uncontrollable seismic sources, are the most effective targets for disaster mitigation.

Earthquakes in marine settings may trigger long chains of secondary hazards. These chains propagate from the seafloor to coastal infrastructure and on to society, yet they have rarely been quantified as a whole. To address this gap, this study proposes a quantitative model of marine seismic hazard chains that combines a catastrophe-dynamics formulation with directed-graph analysis. This network draws on documented secondary hazards from dozens of major tsunamis and includes 39 nodes. This study characterises its topology through degree centrality, network density, centralisation, mean path length, and a layer-resolved net-flow measure. The result is a single-source, dual-hub, multi-sink cascade that is shallow, with most hazards reachable within about two steps. Submarine and coastal earthquakes drive the whole chain, while tsunamis and critical infrastructure failure act as the two high-throughput hubs that amplify and route the flow. Critical network failure is the principal convergence node, where these losses accumulate. The model reveals a monotonic cascade from geological triggers to socioeconomic losses, in which the engineering layer is the gateway through which natural hazards convert into technological failures. A comparison with a mainland earthquake chain at a common taxonomic resolution shows that the two systems share the same four-layer architecture. The mainland earthquake chain exhibits stronger centralisation while the marine tsunami chain presents a more distributed structure. These findings suggest that the controllable engineering hubs, rather than the uncontrollable seismic sources, are the most effective targets for disaster mitigation.

Earthquakes in marine settings may trigger long chains of secondary hazards. These chains propagate from the seafloor to coastal infrastructure and on to society, yet they have rarely been quantified as a whole. To address this gap, this study proposes a quantitative model of marine seismic hazard chains that combines a catastrophe-dynamics formulation with directed-graph analysis. This network draws on documented secondary hazards from dozens of major tsunamis and includes 39 nodes. This study characterises its topology through degree centrality, network density, centralisation, mean path length, and a layer-resolved net-flow measure. The result is a single-source, dual-hub, multi-sink cascade that is shallow, with most hazards reachable within about two steps. Submarine and coastal earthquakes drive the whole chain, while tsunamis and critical infrastructure failure act as the two high-throughput hubs that amplify and route the flow. Critical network failure is the principal convergence node, where these losses accumulate. The model reveals a monotonic cascade from geological triggers to socioeconomic losses, in which the engineering layer is the gateway through which natural hazards convert into technological failures. A comparison with a mainland earthquake chain at a common taxonomic resolution shows that the two systems share the same four-layer architecture. The mainland earthquake chain exhibits stronger centralisation while the marine tsunami chain presents a more distributed structure. These findings suggest that the controllable engineering hubs, rather than the uncontrollable seismic sources, are the most effective targets for disaster mitigation.

Earthquakes in marine settings may trigger long chains of secondary hazards. These chains propagate from the seafloor to coastal infrastructure and on to society, yet they have rarely been quantified as a whole. To address this gap, this study proposes a quantitative model of marine seismic hazard chains that combines a catastrophe-dynamics formulation with directed-graph analysis. This network draws on documented secondary hazards from dozens of major tsunamis and includes 39 nodes. This study characterises its topology through degree centrality, network density, centralisation, mean path length, and a layer-resolved net-flow measure. The result is a single-source, dual-hub, multi-sink cascade that is shallow, with most hazards reachable within about two steps. Submarine and coastal earthquakes drive the whole chain, while tsunamis and critical infrastructure failure act as the two high-throughput hubs that amplify and route the flow. Critical network failure is the principal convergence node, where these losses accumulate. The model reveals a monotonic cascade from geological triggers to socioeconomic losses, in which the engineering layer is the gateway through which natural hazards convert into technological failures. A comparison with a mainland earthquake chain at a common taxonomic resolution shows that the two systems share the same four-layer architecture. The mainland earthquake chain exhibits stronger centralisation while the marine tsunami chain presents a more distributed structure. These findings suggest that the controllable engineering hubs, rather than the uncontrollable seismic sources, are the most effective targets for disaster mitigation.