An Application of Geospatial Based MCDM Technique to Identify Landslide Susceptibility Zones in the Ragnu Khola River Basin of Darjeeling Himalayan Region, India

Several natural disasters are taking place on the earth, and landslide is one of them. Darjeeling Himalaya is one of the world's young fold mountainous area, often suffering from landslide hazards. Hence, the study identifies the landslide susceptibility zone in the Ragnu Khola river basin of the Darjeeling Himalayan region by applying the geospatial-based MCDM technique. This research's major goal is to identify whether this GIS-based multi-criteria decision-making (MCDM) technique is validated or not for landslide susceptibility zones (LSZ); if validated, then how much manifest for describing the LSZ in the study area. MCDM evaluation applies to determining weight value to integrate different thematic layers of river morphometry like Drainage Diversity (DD) parameters and Relief Diversity (RD) parameters. Both DD and RD have significant impacts on landslide intensity. Hence, both layers are combined using the analytical hierarchy process (AHP) of the MCDM technique for the final LSZ. The final result has been validated by ROC analysis using landslide occurring point data obtained from the Geological Survey of India (GSI). The outcome of the study shows that1.45% and 17.83% areas of the region fall in 'very high' and ‘high' LSZ, which belongs to near Mull Gaon, Sanchal forest, and Alubri basty. Most of the area (47.70%) is observed in 'moderate' LSZ. Only 1.32% and 31.7% are kept in ‘very low’ and ‘low’ LSZ, respectively, through the study area. The description capability of the technique for LSZ is significant as the area under the curve (AUC) is 72.10%. The validation of the study using the frequency density of the landslides (FDL) also indicates the 'very high' LSZ is associated with the maximum (2.19/km2) FDL. The work will be needful to develop the overall socio-economic condition of such kind of tectonically sensitive region by proper effective planning.

Basement sliding and the formation of fault systems on Mt Etna volcano

The influence of faulting on the eruptive mechanisms of Mt Etna has been intensively studied, especially regarding the importance of regional tectonics, magma pressure, gravitational spreading and east flank instability. Here we examine the influence of an additional process: the wholesale sliding of the Etna massif along its sloping basement. Using laboratory analogue experiments, we create a series of model volcanoes on sloping basements, with obstructions to represent the mountains and hills surrounding Etna, and an unconstrained downslope edge to represent the unbuttressed seaward slopes. We find that analogues of all the Etna fault systems can be produced in the same model. Furthermore, we find that the relative velocities of transcurrent faulting and extension of each model flank fault system match those of Mt Etna in every case. We also find convincing evidence that gravitational spreading of the summit cone, combined with downslope sliding, controls the position of future eruptive vents around the summit, by creating faults and fractures that form paths of least resistance for magma intrusions. The intruding magma in turn augments fracture opening by an order of magnitude, in a feedback process that dominates within the summit graben. We conclude that gravitational spreading and sliding are the dominant processes in creating faults at Etna, and that these two processes, augmented by magma pressure, are responsible for the rapid seaward movement of the eastern slopes, tectonically cut off from the stable western flanks. The influence of regional tectonism is up to two orders of magnitude lower. The conceptual model derived here could make an important contribution to the investigation and monitoring of eruptive, seismic and landslide hazards, by providing a unified mechanical system that can be used to understand deformation.

Risk Evaluation and Warning Threshold of Unstable Slope Using Tilting Sensor Array

Slope monitoring and early warning systems (EWS) are a promising approach toward mitigating landslide-induced disasters. Many large-scale sediment disasters result in the destruction of infrastructure and loss of human life. The mitigation of vulnerability to slope and landslide hazards will benefit significantly from early warning alerts. The authors have been developing monitoring technology that uses a Micro Electro Mechanical Systems (MEMS) tilt sensor array that detects the precursory movement of vulnerable slopes and informs the issuance of emergency caution and warning alerts. In this regard, the determination of alarm thresholds is very important. Although previous studies have investigated the recording of threshold values by an extensometer which installation of an extensometer at appropriate sites is also difficult. The authors prefer tilt sensors and have proposed a novel threshold for the tilt angle, which was validated in this study. This threshold has an interesting similarity to previously reported viscous models. Additionally, multi-point monitoring has recently emerged and allows for many sensors to be deployed at vulnerable slopes without disregarding the slope’s precursory local behaviour. With this new technology, the detailed spatial and temporal variation of the behaviour of vulnerable slopes can be determined as the displacement proceeds toward failure.

The Relevance of Geotechnical-Unit Characterization for Landslide-Susceptibility Mapping with SHALSTAB

Given the increasing occurrence of landslides worldwide, the improvement of predictive models for landslide mapping is needed. Despite the influence of geotechnical parameters on SHALSTAB model outputs, there is a lack of research on models’ performance when considering different variables. In particular, the role of geotechnical units (i.e., areas with common soil and lithology) is understudied. Indeed, the original SHALSTAB model considers that the whole basin has homogeneous soil. This can lead to the under-or-overestimation of landslide hazards. Therefore, in this study, we aimed to investigate the advantages of incorporating geotechnical units as a variable in contrast to the original model. By using locally sampled geotechnical data, 13 slope-instability scenarios were simulated for the Jaguar creek basin, Brazil. This allowed us to verify the sensitivity of the model to different input variables and assumptions. To evaluate the model performance, we used the Success Index, Error Index, ROC curve, and a new performance index: the Detective Performance Index of Unstable Areas. The best model performance was obtained in the scenario with discretized geotechnical units’ values and the largest sample size. Results indicate the importance of properly characterizing the geotechnical units when using SHALSTAB. Hence, future applications should consider this to improve models’ predictivity.

Evaluation of Landslide Susceptibility of the Ya'an-Linzhi Section of the Sichuan-Tibet Railway based on Deep Learning

The Qinghai-Tibet Plateau is one area with the most frequent landslide hazards due to its unique geology, topography, and climate conditions, posing severe threats to engineering construction and human settlements. The Sichuan-Tibet Railway that is currently under construction crosses the Qinghai-Tibet Plateau; there are frequent landslide disasters along the line, which seriously threaten the construction of the railway. This paper applied two deep learning (DL) algorithms, the convolutional neural network (CNN) and deep neural network (DNN), to landslide susceptibility mapping of the Ya’an-Linzhi section of the Sichuan-Tibet Railway. A geospatial database was generated based on 587 landslide hazards determined by Interferometric Synthetic Aperture Radar (InSAR) Stacking technology, field geological hazard surveys, and 18 landslide influencing factors were selected. The landslides were randomly divided into training data (70%) and validation data (30%) for the modeling training and testing. The Pearson correlation coefficient and information gain method were used to perform the correlation analysis and feature selection of 18 influencing factors. Both models were evaluated and compared using the receiver operating characteristic (ROC) curve and confusion matrix. The results show that better performance in both the training and testing phases was provided by the CNN algorithm (AUC = 0.88) compared to the DNN algorithm (AUC = 0.84). Slope, elevation, and rainfall are the main factors affecting the occurrence of landslides, and the high and very high landslide susceptibilities were primarily distributed in the Jinsha, Lancang, and Nujiang River Basins along the railway. The research results provide a scientific basis for the construction of the Ya'an-Linzhi section of the Sichuan-Tibet Railway within the region, as well as the disaster prevention and mitigation work during future safe operations.