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Groups of landslides induced by heavy rainfall are widely distributed on a global basis and they usually result in major losses of human life and economic damage. However, compared with landslides induced by earthquakes, inventories of landslides induced by heavy rainfall are much less common. In this study we used high-precision remote sensing images before and after continuous heavy rainfall in southern Tianshui, China, from 20 June to 25 July 2013, to produce an inventory of 14,397 shallow landslides. Based on the results of landslide inventory, we utilized machine learning and the geographic information system (GIS) to map landslide susceptibility in this area and evaluated the relative weight of various factors affecting landslide development. First, 18 variables related to geomorphic conditions, slope material, geological conditions, and human activities were selected through collinearity analysis; second, 21 selected machine learning models were trained and optimized in the Python environment to evaluate the susceptibility of landslides. The results showed that the ExtraTrees model was the most effective for landslide susceptibility assessment, with an accuracy of 0.91. This predictive ability means that our landslide susceptibility results can be used in the implementation of landslide prevention and mitigation measures in the region. Analysis of the importance of the factors showed that the contribution of slope aspect (SA) was significantly higher than that of the other factors, followed by planar curvature (PLC), distance to river (DR), distance to fault (DTF), normalized difference vehicle index (NDVI), distance to road (DTR), and other factors. We conclude that factors related to geomorphic conditions are principally responsible for controlling landslide susceptibility in the study area.

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On 18 January 2016, the Zhangjiazhuang high-speed railway tunnel in Ledu, Qinghai Province, China, underwent serious deformation and structural damage. A crack formed at the top of the tunnel and the concrete on the crown peeled off. As a result, the tunnel could not be operated for three months. In order to determine the types and spatial distribution of the landslides in the region and the surface deformation characteristics associated with the tunnel deformation, we used field geological and geomorphological surveys, unmanned aerial vehicle image interpretation and differential interferometric synthetic aperture radar (D-InSAR) surface deformation monitoring. Nine ancient and old landslides were identified and analysed in the study area. Surface deformation monitoring and investigation of buildings in several villages on the slope front showed that the tunnel deformation was not related to deep-seated gravitational slope deformation. However, surface deformation monitoring revealed an active NEE-SWW fault in the area intersecting the tunnel at the location of the tunnel rupture. This constitutes a plausible mechanism for the deformation of the tunnel. Our study highlights the need for detailed engineering geomorphological investigations to better predict the occurrence of tunnel deformation events in the future.