Distribution Modeling and Factor Correlation Analysis of Landslides in the Large Fault Zone of the Western Qinling Mountains: A Machine Learning Algorithm

The area comprising the Langma-Baiya fault zone (LBFZ) and the Bailongjiang fault zone (BFZ) in the Western Qinling Mountains in China is characterized by intensive, frequent, multi-type landslide disasters. The spatial distribution of landslides is affected by factors, such as geological structure, landforms, climate and human activities, and the distribution of landslides in turn affects the geomorphology, ecological environment and human activities. Here, we present the results of a detailed landslide inventory of the area, which recorded a total of 2765 landslides. The landslides are divided into three categories according to relative age, area, and type of movement. Sixteen factors related to geological structure, geomorphology, materials composition and human activities were selected and four machine learning algorithms were used to model the spatial distribution of landslides. The aim was to quantitatively evaluate the relationship between the spatial distribution of landslides and the contributing factors. Based on a comparison of model accuracy and the Receiver Operating Characteristic (ROC) curve, RandomForest (RF) (accuracy of 92%, area under the ROC of 0.97) and GradientBoosting (GB) (accuracy of 96%, area under the ROC curve of 0.97) were selected to predict the spatial distribution of unclassified landslides and classified landslides, respectively. The evaluation results reveal the following. The vegetation coverage index (NDVI) (correlation of 0.2, and the same below) and distance to road (DTR) (0.13) had the highest correlations with the distribution of unclassified landslides. NDVI (0.18) and the annual precipitation index (API) (0.14) had the highest correlations with the distribution of landslides of different ages. API (0.16), average slope (AS) (0.14) and NDVI (0.1) had the highest correlations with the landslide distribution on different scales. API (0.28) had the highest correlation with the landslide distribution based on different types of landslide movement.

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

Construction of the 998.64-km Linzhi–Ya’an section of the Sichuan–Tibet Railway has been influenced by landslide disasters, threatening the safety of Sichuan–Tibet railway projects. Landslide identification and deformation analysis in this area are urgently needed. In this context, it was the first time that 164 advanced land-observing satellite-2 (ALOS-2) phased array type L-band synthetic aperture radar-2 (PALSAR-2) images were collected to detect landslide disasters along the entire Linzhi–Ya’an section. Interferogram stacking and small baseline interferometry methods were used to derive the deformation rate and time-series deformation from 2014–2020. After that, the hot spot analysis method was introduced to conduct spatial clustering analysis of the annual deformation rate, and the effective deformation area was quickly extracted. Finally, 517 landslide disasters along the Linzhi–Ya’an route were detected by integrating observed deformation, Google Earth optical images, and external geological data. The main factors controlling the spatial landslide distribution were analyzed. In the vertical direction, the spatial landslide distribution was mainly concentrated in the elevation range of 3000–5000 m, the slope range of 10–40°, and the aspect of northeast and east. In the horizontal direction, landslides were concentrated near rivers, and were also closely related to earthquake-prone areas, fault zones, and high-precipitation areas. In short, rainfall, freeze–thaw weathering, seismic activity, and fault zones are the main factors inducing landslides along this route. This research provides scientific support for the construction and operation of the Linzhi–Ya’an section of the Sichuan–Tibet Railway.

A database of submarine landslides offshore West and Southwest Iberia

Submarine landslides are major geohazards occurring on distinct seabed domains ranging from shallow coastal areas to the deeper points of the ocean. The nature and relief of the seabed are key factors influencing the location and size of submarine landslides. Efforts have recently been made to compile databases of submarine landslide distribution and morphometry, a crucial task to assess submarine geohazards. The MAGICLAND (Marine Geo-hazards Induced by underwater Landslides in the SW Iberian Margin) database here presented contributed to that assessment offshore Portugal. Based on EMODnet bathymetric DEMs and GIS analysis, the morphometric properties of 1552 submarine landslides were analysed and wealth of 40 parameters was obtained. This dataset is now made available for the free use and benefit of the international marine community. Further contributions or analysis based on, and complementing the MAGICLAND database will be welcome.

Spatial Patterns and Scaling Distributions of Earthquake-Induced Landslides—A Case Study of Landslides in Watersheds Along Dujiangyan–Wenchuan Highway

Earthquake-induced landslide has various spatial characteristics that can be effectively described with the frequency–area curve. Nevertheless, the widely used power-law curve does not reflect well the spatial features of the distribution, and the power exponent does not show the association with the background factors. There is a lack of standards for building the relationship, and its implication on the spatial distribution of landslides has never been analyzed. In this study, we propose a new form of frequency distribution and explore the parameters in the typical watersheds along the highway from Dujiangyan to Wenchuan in the Wenchuan earthquake region. The obtained parameters are related to the landslide density and proportions of the large-scale landslides. Furthermore, a hot spot analysis of landslides in the watersheds is conducted to assess the relationship between the parameters and the spatial cluster patterns of landslides. The hot spots highlight the size and distance of landslide areas that cluster together, whereas the distribution parameters reflect the density and proportions of landslides. This research introduces a new method to analyze the distribution of landslides and their association with the spatial features, which can be applied to the landslide distribution in relation to other influential factors.

Landslide Investigation Results in Sapa Town, Lao Cai Province, Vietnam in December 2019

Every year, especially in the rainy season, landslides occur quite often in Lao Cai – a northern mountainous province of Vietnam. Specifically, in the year 2019, several landslides were observed to occur near the Sapa Ancient Rock Field in Hau Thao commune, Sapa town, Lao Cai province. In December 2019, a landslide investigation was conducted to examine the mechanism and possible causes of the landslides. Besides that, as the landslide distribution in this area is still unclear, this study will also aim to show the landslide denseness in a 700 m × 700 m square map as well as survey results in 2019 of two main landslides in such map. According to the survey, the landslide is the main phenomenon of geomorphological development in this area, being a combination of multiple different landslides with varying sizes and dissimilar triggers. The first survey landslide is about 50 m wide and 350 m long and has still been going on in recent years, with annual horizontal displacement being around 0.8 m. Meanwhile, the second one is a typical flash-landslide caused by rainfall. Despite being quite small in scale, about 15 m × 40 m, its characteristics indicate a dangerous implication in the future. This information will be the basis for further ongoing studies.

Combining TRIGRS and Massflow Models for the Assessment of Flash Flood Disasters Related to Sediment Supply in Mountainous Watersheds

<p>Abstract: Evaluation of a large number of rainstorm disasters shows that the coupling effect of sediment supply and floodwaters is one predominant cause for the occurrence of flash flood disasters. Rainfall-induced shallow landslides often provide an adequate source of solid materials to recharge moving sediment during flash floods. In this study, we used the TRIGRS model to analyze the rainfall-related landslide stability in a mountainous basin and gain potential landslide volumes as potential sources for sediment loads. Then, with the calculated results of landslides as input, the Massflow model was used to evaluate how the landslides as sediment loads evolved with flows. The results showed that there was a large amount of sediment deposited in the channel, which can be initiated and transported by heavy rainfalls, leading to the destruction of villages at the mouth of gullies. In general, this study offers a strategy of evaluating sediment-coupled flash flood disasters that the TRIGRS can provides the estimate of landslide distribution and volume first and the Massflow provides the estimate of subsequent movement of the solids caused by flash floods.</p>