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Landslides are the most destructive geological hazard in the hilly regions. For systematic landslide mitigation and management, landslide evaluation and hazard zonation is required. Over the past few decades several techniques have been developed that can be used for landslide evaluation and zonation. These techniques can broadly be classified into qualitative and quantitative approaches. Qualitative approaches include geomorphological analysis and heuristic techniques whereas quantitative approaches include statistical, artificial intelligence and deterministic techniques. In quantitative techniques prediction for landslide susceptibility is based on the actual realistic data and interpretations. Further, the quantitative techniques also overcome the subjectivity of qualitative approaches. Each of these techniques may consider different causative factors and utilizes various means for factor evaluation and analysis. When compared, each of these techniques has its own advantage and disadvantage over other techniques. The selection of appropriate technique for landslide hazard evaluation and zonation is very crucial. The factors that need to be considered to adopt an appropriate approach are; investigation purpose, the extent of the area to be covered, the type of mapping units, the scale of map to be produced, type of data to be used, type of landslides, availability of resources, capability and skill set of an evaluator and the accessibility to the study area. The main aim of this article is to present a comprehensive review on various techniques and approaches available for landslide susceptibility and hazard zonation mapping. Further, attempt is also made to assess the effectiveness of these techniques in landslide hazard zonation studies.

Landslides often cause deaths and severe economic losses. In general, forests play an important role in reducing landslide probability because of the stabilizing effect of the tree roots. Although fruit groves consist of trees, which are similar to forests, practical land management, such as the frequent trampling of fields by laborers and compression of the terrain, may cause such land to become prone to landslides compared with forests. Fruit groves are widely distributed in hilly regions, but few studies have examined their role in landslide initiation. This study aims at filling this gap evaluating the predisposing and triggering conditions for rainfall-triggering landslides in part of Uwajima City, Japan. A large number of landslides occurred due to a heavy rainfall event in July 2018, where citrus groves occupied about 50% of the study area. In this study, we combined geodata with a regression model to assess the landslide hazard of fruit groves in hilly regions. We developed maps for five conditioning factors: slope gradient, slope aspect, normalized difference vegetation index (NDVI), land use, and geology. Based on these five maps and a landslide inventory map, we found that the landslide area density in citrus groves was larger than in forests for the categories of slope gradient, slope aspect, NDVI, and geology. Ten logistic regression models along with different rainfall indices (i.e., 1-h, 3-h, 12-h, 24-h maximum rainfall and total rainfall) and different land use (forests or citrus groves) in addition to the other four conditioning factors were produced. The result revealed that “citrus grove” was a significant factor with a positive coefficient for all models, whereas “forest” was a negative coefficient. These results suggest that citrus groves have a higher probability of landslide initiation than forests in this study area. Similar studies targeting different sites with various types of fruit groves and several rainfall events are crucial to generalize the analysis of landslide hazard in fruit groves.

Landslide susceptibility evaluation (LSE) refers to the probability of landslide occurrence in a region under a specific geological environment and trigger conditions, which is crucial to preventing and controlling landslide risk. The mainstream of the Yangtze River in Yichang City belongs to the largest basin in the Three Gorges Reservoir area and is prone to landslides. Affected by global climate change, seismic activity, and accelerated urbanization, geological disasters such as landslide collapses and debris flows in the study area have increased significantly. Therefore, it is urgent to carry out the LSE in the Yichang section of the Yangtze River Basin. The main results are as follows: (1) Based on historical landslide catalog, geological data, geographic data, hydrological data, remote sensing data, and other multi-source spatial-temporal big data, we construct the LSE index system; (2) In this paper, unsupervised Deep Embedding Clustering (DEC) algorithm and deep integration network (Capsule Neural Network based on SENet: SE-CapNet) are used for the first time to participate in non-landslide sample selection, and LSE in the study area and the accuracy of the algorithm is 96.29; (3) Based on the constructed sensitivity model and rainfall forecast data, the main driving mechanisms of landslides in the Yangtze River Basin were revealed. In this paper, the study area’s mid-long term LSE prediction and trend analysis are carried out. (4) The complete results show that the method has good performance and high precision, providing a reference for subsequent LSE, landslide susceptibility prediction (LSP), and change rule research, and providing a scientific basis for landslide disaster prevention.

Catastrophic landslides have much more frequently occurred worldwide due to increasing extreme rainfall events and intensified human engineering activity. Landslide susceptibility evaluation (LSE) is a vital and effective technique for the prevention and control of disastrous landslides. Moreover, about 80% of disastrous landslides had not been discovered ahead and significantly impeded social and economic sustainability development. However, the present studies on LSE mainly focus on the known landslides, neglect the great threat posed by the potential landslides, and thus to some degree constrain the precision and rationality of LSE maps. Moreover, at present, potential landslides are generally identified by the characteristics of surface deformation, terrain, and/or geomorphology. The essential disaster-inducing mechanism is neglected, which has caused relatively low accuracies and relatively high false alarms. Therefore, this work suggests new synthetic criteria of potential landslide identification. The criteria involve surface deformation, disaster-controlling features, and disaster-triggering characteristics and improve the recognition accuracy and lower the false alarm. Furthermore, this work combines the known landslides and discovered potential landslides to improve the precision and rationality of LSE. This work selects Chaya County, a representative region significantly threatened by landslides, as the study area and employs multisource data (geological, topographical, geographical, hydrological, meteorological, seismic, and remote sensing data) to identify potential landslides and realize LSE based on the time-series InSAR technique and XGBoost algorithm. The LSE precision indices of AUC, Accuracy, TPR, F1-score, and Kappa coefficient reach 0.996, 97.98%, 98.77%, 0.98, and 0.96, respectively, and 16 potential landslides are newly discovered. Moreover, the development characteristics of potential landslides and the cause of high landslide susceptibility are illuminated. The proposed synthetic criteria of potential landslide identification and the LSE idea of combining known and potential landslides can be utilized to other disaster-serious regions in the world.

In landslide susceptibility evaluation, scientific sampling minimizes potential societal losses and enhances the efficiency of disaster prevention and mitigation. However, traditional sampling methods, such as selecting landslide and non-landslide samples based on equal proportions or area proportions, overlook the different societal losses resulting from landslide omission and misreporting, and the potential societal losses faced by their evaluation results are often not minimized. Therefore, this study proposes a sampling method that takes potential societal losses into account and uses the Landslide Misjudgment Potential Societal Loss Evaluation Index (LMPSLEI) to quantify the total potential social losses in the area due to landslide omission and misreporting. The LMPSLEI is minimized by optimizing the sample ratio, thus minimizing the potential societal losses faced by the evaluation results and enhancing the scientific basis of disaster prevention and mitigation efforts. This study takes the Wenchuan earthquake area as the research region, selects 13 conditional factors and employs two models—Random Forest (RF) and Convolutional Neural Network (CNN)—to conduct case studies. We derive the recommended sample ratio based on the formula, hypothesizing that the LMPSLEI will be minimized under this ratio. The results show that the sample ratio for LMPSLEI minimization in the RF model is similar to the recommended sample ratio, while the sample ratio for LMPSLEI minimization in the CNN model is slightly higher than the recommended sample ratio. The recommended sample ratio can achieve the minimum of LMPSLEI or reach a lower value under different societal losses weights of landslide omission/misreporting, and thus it can be used as a preliminary choice of sampling for landslide susceptibility evaluation considering the potential societal losses.

The southwest region of China is characterized by numerous rugged mountains and valleys, which create favorable conditions for landslide disasters. The landslide-influencing factors show different sensitivities regionally, which induces the occurrence of disasters to different degrees, especially in small sample areas. This study constructs a framework for the identification, analysis, and evaluation of landslide hazards in complex mountainous regions within small sample areas. This study utilizes small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology and high-resolution optical imagery for a comprehensive interpretation to identify landslide hazards. A geodetector is employed to analyze disaster-inducing factors, and machine-learning models such as random forest (RF), gradient boosting decision tree (GBDT), categorical boosting (CatBoost), logistic regression (LR), and stacking ensemble strategies (Stacking) are applied for landslide sensitivity evaluation. GMLCM stands for geodetector–machine-learning-coupled modeling. The results indicate the following: (1) 172 landslide hazards were identified, primarily concentrated along the banks of the Lancang River. (2) A geodetector analysis shows that the key disaster-inducing factors for landslides include a digital elevation model (DEM) (1321–1857 m), rainfall (1181–1290 mm/a), the distance from roads (0–1285 m), and geological rock formation (soft rock formation). (3) Based on the application of the K-means clustering algorithm and the Bayesian optimization algorithm, the GD-CatBoost model shows excellent performance. High-sensitivity zones were predominantly concentrated along the Lancang River, accounting for 24.2% in the study area. The method for identifying landslide hazards and small-sample sensitivity evaluation can provide guidance and insights for landslide monitoring and harnessing in similar geological environments.

The present research was conducted in the town of Debre Werk, East Gojjam, North West Ethiopia. This study aimed to apply and validate Slope Stability Susceptibility Evaluation Parameter (SSEP) rating system and produce a landslide hazard zonation (LHZ) map of the area. This rating system was done by considering the parameters of intrinsic and external triggering factors that cause landslides. Systematic and detailed fieldwork had been undertaken as a justification. Secondary data, on the other hand, were required to define the general conditions of the area and to gain a thorough understanding of the field of study. Ratings for intrinsic parameters in the SSEP system include slope morphometry, relative relief, slope content, geological structures/discontinuities, land use land cover, groundwater, and external parameters include erosion, seismicity, and manmade activities. Individual facet-wise ratings for intrinsic causative factors and external triggering factors ratings are summarized to evaluate the landslide hazard zonation of an environment. The sum of all causative parameter ratings will give evaluated landslide hazards. Therefore, the research was carried out by dividing the study area into 70 facets. A landslide inventory including 85 landslide activities was prepared. Thus, 23, 39, and 23 landslide activities were identified as active landslide, past landslides, and signs of landslide, respectively. The delineated 70 facets were categorized into three landslide hazard zones. There are about 73.3 km 2 (27.2%) of the study area within the low hazard zone, 140.8 km 2 (52.1%) within the moderate hazard zone, and the remaining 55.9 km 2 (20.7%) within the high hazard zone. Based on the findings of SSEP, it can be deduced that the present research area is highly susceptible to landslides and requires special attention during rainy seasons. Finally, the validity of the prepared LHZ map was checked by overlaying the inventory map over the produced LHZ map. The results were compared with the actual active landslide activity data in the area. The overlay analysis reveals that out of a total of 23 active landslide locations, 19 (82.6%) fall within the ‘high hazard zone,’ whereas the remaining 4 (17.4%) fall within the ‘moderate hazard zone.’ The validation of the prepared LHZ map suggests that the applications of the SSEP rating system provide a good basis to produce produced LHZ maps.

This paper compares the findings of macrolevel landslide hazard zonation carried out along the highway from Bhalukpong to Bomdila, West Kameng district, Arunachal Pradesh following GSI and BIS guidelines. The map resulted from the GSI guideline shows that 69.31% of the faceted area falls under the Low Hazard Zone (LHZ) while 17.69%, 7.31%, 5.03% and 0.65% of the area are in Moderate Hazard Zone (MHZ), High Hazard Zone (HHZ), Very Low Hazard Zone (VLHZ) and Very High Hazard Zone (VHHZ) respectively. Correlation between the landslide incidences and different hazard zones reveals that maximum failure percentage is in VHHZ and it is followed by HHZ, MHZ and LHZ. The second map resulting from BIS guideline reveals that 45.77% of the faceted area falls under MHZ while 41.39%, 11.52% and 1.29% of the area are in HHZ, LHZ and VHHZ respectively. Not a single facet falls in VLHZ. With regard to failure percentage VHHZ experiences 50%, while that of HHZ, MMH and LHZ is roughly 11.5% each. In the study area, the landslide hazard zonation map resulting from GSI guideline broadly conforms to field condition. It may be due to the fact that the study area is along the road corridor where slope cutting and landslides are very common and GSI guideline considers both the slope cutting and landslide parameters, while it is not so in the case of BIS guidelines. However, a final conclusion can be drawn after carrying out such studies in different geological settings. Copyright 2014 Geological Society of India

The challenge of obtaining landslide susceptibility zoning in Tibet is compounded by the high altitude, extensive range, and difficult exploration of the region. To address this issue, a novel evaluation approach based on Stacking ensemble machine learning is proposed. This study focuses on Jiacha County, adopts the slope unit as the evaluation unit, and picks up 14 evaluation factors that symbolize the topography and geomorphology, environmental and hydrological features, and basic geological features. These landslide conditioning factors were integrated into a total of 4660 Stacking ensemble learning models, randomly combined by 10 base-algorithms, including AdaBoost, Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), k-Nearest Neighbors (kNNs), LightGBM, Multilayer Perceptron (MLP), Random Forest (RF), Ridge Regression, Support Vector Machine (SVM), and XGBoost. All models were trained, using the natural discontinuity method to classify landslide susceptibility, and the AUC value, the area under the ROC curve, was taken to evaluate the model. The results show that the maximum AUC values in the 9 models performing better reach 0.78 and 0.99 over the test set and the train set. Most of the areas identified as high susceptibility and above show consistency with the interpretation of the existing geological field data. Thus, the Stacking ensemble method is applicable to the landslide susceptibility situation in Jiacha County, Tibet, and can provide theoretical support for disaster prevention and mitigation work in the Qinghai–Tibet Plateau area.

Landslide susceptibility evaluation is an indispensable part of disaster prevention and mitigation work. Selecting effective evaluation methods and models for landslide susceptibility assessment is of significant importance. This study focuses on selected areas in Yunyang County, Chongqing City. By interpreting high-resolution satellite remote sensing images from before and after heavy rainfall on 31 August 2014, the distribution of rainfall-induced accumulation landslides was obtained. To evaluate the susceptibility of accumulation landslides, we have equated evaluation factors to accumulation distribution prediction factors. Eight evaluation factors were extracted using multi-source data, including lithology, elevation, slope, remote sensing image texture features, and the normalized difference vegetation index (NDVI). Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. Subsequently, the accuracy of the evaluation models was compared and verified using the Receiver Operating Characteristic (ROC) curve, and the evaluation results were analyzed. Finally, the developed Random Forest model was applied to Gongping Town in Fengjie County to verify its applicability in other regions. The findings indicate that the complex geological conditions and the unique tectonic erosion landform patterns in the northeastern region of Chongqing not only make this area a center of heavy rainfall but also lead to frequent and recurrent rainfall-induced landslides. The Random Forest model effectively reflects the development characteristics of accumulation landslides in the study area. High and very high susceptibility zones are concentrated in the northern and central regions of the study area, while low and moderate susceptibility zones predominantly occupy the mountainous and riverside areas. Landslide susceptibility mapping in the study area shows that the Random Forest model yields reasonably graded results. Elevation, remote sensing image texture features, and lithology are highly significant factors in the evaluation system, indicating that the development factors of slope geological disasters in the study area are mainly related to topography, geomorphology, and lithology. The landslide susceptibility evaluation results in Gongping Town, Fengjie County, validate the applicability of the Random Forest model developed in this study to other regions.