Debris flow susceptibility mapping is considered to be useful for hazard prevention and mitigation. As a frequent debris flow area, many hazardous events have occurred annually and caused a lot of damage in the Sichuan Province, China. Therefore, this study attempted to evaluate and compare the performance of four state-of-the-art machine-learning methods, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Boosted Regression Trees (BRT), for debris flow susceptibility mapping in this region. Four models were constructed based on the debris flow inventory and a range of causal factors. A variety of datasets was obtained through the combined application of remote sensing (RS) and geographic information system (GIS). The mean altitude, altitude difference, aridity index, and groove gradient played the most important role in the assessment. The performance of these modes was evaluated using predictive accuracy (ACC) and the area under the receiver operating characteristic curve (AUC). The results of this study showed that all four models were capable of producing accurate and robust debris flow susceptibility maps (ACC and AUC values were well above 0.75 and 0.80 separately). With an excellent spatial prediction capability and strong robustness, the BRT model (ACC = 0.781, AUC = 0.852) outperformed other models and was the ideal choice. Our results also exhibited the importance of selecting suitable mapping units and optimal predictors. Furthermore, the debris flow susceptibility maps of the Sichuan Province were produced, which can provide helpful data for assessing and mitigating debris flow hazards.

The sliding-block model is often used for the prediction of permanent coseismic displacements of natural slopes and earth structures. This model assumes motion in an inclined plane but does not consider the decrease in inclination of the sliding soil mass as a result of its downward motion, which is the usual condition in the field. The paper studies the above effect and proposes an empirical equation correcting the predictions of the sliding-block model. The investigation is performed by using a recently developed multiblock model. The equation correcting the predictions of the sliding-block model depends on the slip length, the difference in inclinations of the upper and lower part of the slip surface, the seismic displacement predicted by the sliding-block model and the maximum value of the applied horizontal acceleration.

The Eastern Thessaly Plain presents an area of severe settlement phenomena, owing to the over-exploitation of the underground aquifer systems, causing significant damages to national infrastructures and private properties annually. Herein, both Persistent Scatterers (PS) and Small Baselines (SB) interferometric techniques were applied to study the history of ground deformation along the entire plain. Although the area consisted mostly of agricultural land, a sufficient number of point targets was obtained, well-distributed over the entire plain, permitting the recognition of spatial variations of the displacement field in addition to temporal trends. Our findings outline the southern part of the basin as the mostly affected area, whereas local subsidence patterns of lower magnitude were also recognized elsewhere. Episodes of significant ground subsidence, reaching several centimetres within a few months, characterize the deformation pattern of the area. Although average ground deformation rates do not exceed 2 cm year⁻¹, line-of-sight (LOS) displacements of up to 13 cm were observed, occurring during the summer–autumn periods. A geographic information system (GIS)-based post-processing approach for the analysis of synthetic aperture radar (SAR) time series is presented, by which these abrupt settlement episodes can be identified in both temporal and spatial domains. The analysis allows the separation between rapid subsidence phenomena during the summer–fall season and annual deformation rates, thereby providing valuable information regarding the actual deformation pattern of the area. The results confirm in situ geological observations, highlighting the unique behaviour of the area due to intense water pumping. The study underlines that average SAR displacement rate maps might be inadequate to describe complex deformation scenarios and could lead to misinterpretations. Exploitation of the full capacity of SAR time series by detailed examination of the displacement histories, through a tailored data-mining strategy, could provide valuable information to geotechnical engineers and planners.