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Landslide susceptibility assessment is vital for landslide risk management and urban planning, and the scientific community is continuously proposing new approaches to map landslide susceptibility, especially by hybridizing state-of-the-art models and by proposing new ones. A common practice in landslide susceptibility studies is to compare (two or more) different models in terms of AUC (area under ROC curve) to assess which one has the best predictive performance. The objective of this paper is to show that the classical scheme of comparison between susceptibility models can be expanded and enriched with substantial geomorphological insights by focusing the comparison on the mapped susceptibility values and investigating the geomorphological reasons of the differences encountered. To this aim, we used four susceptibility maps of the Wanzhou County (China) obtained with four different classification methods (namely, random forest, index of entropy, frequency ratio, and certainty factor). A quantitative comparison of the susceptibility values was carried out on a pixel-by-pixel basis, to reveal systematic spatial patterns in the differences among susceptibility maps; then, those patterns were put in relation with all the explanatory variables used in the susceptibility assessments. The lithological and morphological features of the study area that are typically associated to underestimations and overestimations of susceptibility were identified. The results shed a new light on the susceptibility models, identifying systematic errors that could be probably associated either to shortcomings of the models or to distinctive morphological features of the test site, such as nearly flat low altitude areas near the main rivers, and some lithological units. [ABSTRACT FROM AUTHOR]

This paper reports on a typical slow-moving landslide that occurred in Shuitianba village in Wanyuan County, Sichuan Province, China, on September 16, 2017. The landslide in Wanyuan County is regionally outstanding for its features and well-developed landslide morphology. Hence, this event can provide a good case study for understanding the possible triggering conditions of the occurrences of all the landslides examined in Wanyuan County. The 225 residents of Shuitianba village did not flee the village because the deformation of the landslide was extremely slow. However, the primary emergency tasks are still necessary to reduce the landslide risk that may affect the residents. Detailed field investigations, boreholes, and exploratory trenches were performed to investigate the characteristics and triggering factors of this landslide. Rainfall and anthropogenic activities (such as informal, unplanned settlements and deforestation) were revealed to be the main triggering factors. The main engineering control measures, including anti-sliding piles, an anti-sliding retaining wall, and intercepting ditch, were applied to stabilize the landslide. However, landslide mitigation measures have not been finished, and hence, some interim measures are suggested to effectively reduce landslide risk. [ABSTRACT FROM AUTHOR]

Based on former conceptual models of vulnerability, this paper aims to improve the quantitative model for regional vulnerability assessment by analyzing in-depth the relation between vulnerability, exposure, coping capacity, and resilience. Taking the mountain settlements in the upper reaches of Min River, China, as a case study, the method of Contributing Weight Superposition (CWS) is applied in establishing both a model and a system for the vulnerability assessment of elements at risk. The CWS approach consists of 13 index factors including population, economic and road densities, building and farmland coverage, hazard-affected areas, urbanization rate, and GDP per capita. Accordingly, a debris flow hazard vulnerability zoning map was obtained and the assessment results show that the distribution of high and comparatively high vulnerability zones, where economic activities are considerably high, has a close correlation to the topography of the catchment and population characteristics. The results thus may serve as a pertinent guidance for settlement relocation, population distribution readjustment, and management to prevent and reduce hazards in the upper reaches of Min River and beyond. [ABSTRACT FROM AUTHOR]

On June 24, 2017, a catastrophic landslide (Xinmo landslide) occurred on the left bank of Songping river in Diexi town, Sichuan Province, China. Based on field investigations, this paper tries to reveal the cause and mechanism of the initiation and development of the Xinmo landslide. Xinmo landslide is located in the so called “Minshan block.” This tectonic block is very active and generates many earthquakes. Among them, the 1933 Diexi Ms 7.5 earthquake which had an indispensable effect on the occurrence of the Xinmo landslide, whose distance to the recent Xinmo landslide is only 8.7 km. The 1933 earthquake triggered the collapse of the Qianxin gully, which damaged the rock mass forming the source of the Xinmo landslide and creating a free prominent ridge. The later 1976 Songpan Ms 7.2 earthquake and the 2008 Wenchuan Ms 8.0 earthquake further damaged the integrity of the rock mass in the source area of the 2017 Xinmo landslide. The Xinmo landslide developed on a typical bedding dip slope with metasandstone intercalated with a few thin bedded slate layers. The slate intercalation gives the slope a very low shear strength in the dip direction and the long term rainfall may have softened the slip zone and the locked section. These two aspects have promoted the occurrence of Xinmo landslide. [ABSTRACT FROM AUTHOR]

On May 12, 2019, a landslide occurred near a construction site of a reservoir dam along the left bank of the Wulong River, Qinghai Province, China. Video footage showed that the entire failure process of the landslide lasted for 15 min. Although an excavator that parked on the foundation pit was buried and the Wulong River was blocked, this landslide did not cause any casualties. Based on field investigations, 3D laser scanning, unmanned aerial vehicle observations, and borehole surveys, this study aims to reveal the cause and initiation mechanisms of the landslide. The landslide developed on a typical dip slope with thick-bedded conglomerates intercalated with a few mudstone layers. The slide mass mainly consisted of conglomerates with a dip angle varying from 20 to 40°, failing along bedding plane contacts with the underlying muddy intercalation. Numerous tension cracks had developed in the upper part of the slope due to stress relaxation during the process of bank slope evolution associated with rapid river incision. These cracks damaged the integrity of the rock mass and promoted rapid rainfall infiltration. The water infiltrated into the slope and deteriorated the mechanical properties of the rock mass, which resulted in long-term deterioration of the slope stability conditions. In addition, the temperature of Tongren County was below zero for 5 months in a year, hence the freeze–thaw cycle of the water filled in the cracks would have reduced the shear strength further. There were 10 days of continuous rainfall before the landslide occurred, and the cumulative precipitation in 2018 and 2019 was also significantly more than that of previous years. Three primary factors are involved in the landslide event: (1) thick-bedded conglomerates intercalated with a few mudstone layers with contrasts in strength, (2) development of numerous relaxation cracks, and (3) long-term rainfall accumulation. [ABSTRACT FROM AUTHOR]

A large number of slope movements occur in China annually. Especially, fatal landslides are the most hazardous, causing serious fatalities and significant socio-economic losses. In this study, we collected data on fatal landslides triggered by non-seismic effects from China’s geological environment information site and Ministry of Natural Resources of China for the period 2004-2016. Then, we carried out a statistical analysis of the data to explore the trend and spatiotemporal distribution of the fatal landslides, as well as the distribution of its losses in economic and fatality terms. In the studied period, a total of 4718 deaths were recorded as resulting from 463 landslide events. It represents a frequency of 36 events and an average of 363 deaths every year. Also, an increasing trend of such landslide is observed in the period 2011-2016 with hazard record improvement. But its economic loss has a decreasing proportion of all recorded non-seismic geohazard loss for this period. Even so, the total economic loss in the studied period is still enormous at $981.29 million. The spatial distribution of fatal landslides shows intensive clusters in southwestern and southern China due to the possible distinctive geological environment and precipitation conditions. The temporal distribution reveals significant association with the rainy season, with the largest quantity of events occurring between June and September. Among all the collected landslides during the studied period, 94.2% are associated with rainfall. This research gives a comprehensive recognition of fatal landslide damage and provides baseline information for landslide prevention and mitigation. [ABSTRACT FROM AUTHOR]

The main objective of the present study was to produce a landslide susceptibility map by implementing a novel methodology that combines Information Theory and GIS-based methods for the Nancheng County, China, an area with numerous reported landslide events. Specifically, the information coefficient that is estimated from Shannon's entropy index was used to determine the number of classes of each landslide-related variable that maximizes the information coefficient, while three methods, logistic regression, weight of evidence, and random forest algorithm, were implemented to produce the landslide susceptibility map. The comparison of the various models was based on the assessment of a database of 112 past landslide events, which were divided randomly into a training dataset (70 %) and a validation dataset (30 %). The identification of the areas affected was established by analyzing airborne imagery, extensive field investigation, and the examination of previous research studies, while the morphometric variables were derived using remote sensing technology. The geo-environmental conditions in those locations were analyzed regarding their susceptibility to slide. In particular, 11 variables were analyzed: lithology, altitude, slope, aspect, topographic wetness index, sediment transport index, profile curvature, plan curvature, distance to rivers, distance to faults, and distance to roads. The comparison and validation of the outcomes of each model were achieved using statistical evaluation measures, the receiving operating characteristic, and the area under the success and predictive rate curves. Each model gave similar outcomes; however, the random forest model had a slightly higher predictive performance in terms of area under the curve (0.9220) against the ones estimated for the weight of evidence (0.9090) and the logistic regression model (0.8940). The same pattern of performance was reported when the success power of the models was calculated. Random forest was slightly better than the other two models in terms of area under the curve (0.9350) in comparison with the weight of evidence (0.9255) and logistic regression (0.9097). The predictive performance was estimated by using the validation dataset, while the success power of the models was estimated by using the training dataset. From the visual inspection of the produced landslide susceptibility maps, the most susceptible areas are located at the west and east mountainous areas, while moderate to low susceptibility values characterize the central area. [ABSTRACT FROM AUTHOR]