Abstract. Seismic-induced landslide hazards are studied using seismic shaking intensity based on the topographic amplification effect. The estimation of the topographic effect includes the theoretical topographic amplification factors and the corresponding amplified ground motion. Digital elevation models (DEM) with a 5-m grid space are used. The logistic regression model and the geographic information system (GIS) are used to perform the seismic landslide hazard analysis. The 99 Peaks area, located 3 km away from the ruptured fault of the Chi-Chi earthquake, is used to test the proposed hypothesis. An inventory map of earthquaketriggered landslides is used to produce a dependent variable that takes a value of 0 (no landslides) or 1 (landslides). A set of independent parameters, including lithology, elevation, slope gradient, slope aspect, terrain roughness, land use, and Arias intensity (I a ) with the topographic effect. Subsequently, logistic regression is used to find the best fitting function to describe the relationship between the occurrence and absence of landslides within an individual grid cell. The results of seismic landslide hazard analysis that includes the topographic effect (AUROC = 0.890) are better than those of the analysis without it (AUROC = 0.874).
Pre-seismic gravity anomalies from records obtained at a 1 Hz sampling rate from superconducting gravimeters (SG) around East Asia are analyzed. A comparison of gravity anomalies to the source parameters of associated earthquakes shows that the detection of pre-seismic gravity anomalies is constrained by several mechanical conditions of the seismic fault plane. The constraints of the far-field pre-seismic gravity amplitude perturbation were examined and the critical spatial relationship between the SG station and the epicenter precursory signal for detection was determined. The results show that: (1) the preseismic amplitude perturbation of gravity is inversely proportional to distance; (2) the transfer path from the epicenter to the SG station that crosses a tectonic boundary has a relatively low pre-seismic gravity anomaly amplitude; (3) the pre-seismic gravity perturbation amplitude is also affected by the attitude between the location of an SG station and the strike of the ruptured fault plane. The removal of typhoon effects and the selection of SG stations within a certain intersection angle to the strike of the fault plane are essential for obtaining reliable pre-seismic gravity anomaly results.
Landslides have been identified as one of the costliest and deadliest natural disasters, causing tremendous damage to humans and societies. Information regarding the spatial extent of landslides is thus important to allow officials to devise successful strategies to mitigate landslide hazards. This study aims to develop a machine-learning approach for predicting landslide areas in the Tsengwen River Watershed (TRW), which is one of the most landslide-prone areas in Central Taiwan. Various spatial datasets were collected from 2009 to 2015 to derive 36 predictive variables used for landslide modeling with random forests (RF). The results of landslide prediction, compared with ground reference data, indicated an overall accuracy of 91.4% and Kappa coefficient of 0.83, respectively. The findings achieved from estimates of predictor importance also indicated to officials that the land-use/land-cover (LULC) type, distance to previous landslides, distance to roads, bank erosion, annual groundwater recharge, geological line density, aspect, and slope are the most influential factors that trigger landslides in the study region.
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