Probability Of Landslide Occurrence Research Articles

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation. This study presents a machine learning approach based on the C5.0 decision tree (DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data (70% landslide pixels) and validation data (30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model. Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC (area under the receiver operating characteristic (ROC) curve) of the proposed model was the highest, reaching 0.88, compared with traditional models (support vector machine (SVM) = 0.85, Bayesian network (BN) = 0.81, frequency ratio (FR) = 0.75, weight of evidence (WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km2 and 0.88/km2, respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area. Our results indicate that the distribution of high susceptibility zones was more focused without containing more “stable” pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

Using landslide-inventory mapping for a combined bagged-trees and logistic-regression approach to determining landslide susceptibility in eastern Kentucky, USA

High-resolution LiDAR-derived datasets from a 1.5 m digital elevation model and a detailed landslide inventory ( n ≥ 1000) for Magoffin County, Kentucky, USA were used to develop a combined machine-learning and statistical approach to improve geomorphic-based landslide-susceptibility mapping. An initial dataset of 36 variables was compiled to investigate the connection between slope morphology and landslide occurrence. Bagged trees, a machine-learning random-forest classifier, was used to evaluate the geomorphic variables, and 12 were identified as important: standard deviation of plan curvature, standard deviation of elevation, sum of plan curvature, minimum slope, mean plan curvature, range of elevation, sum of roughness, mean curvature, sum of curvature, mean roughness, minimum curvature and standard deviation of curvature. These variables were further evaluated using logistic regression to determine the probability of landslide occurrence and then used to create a landslide-susceptibility map. The performance of the logistic-regression model was evaluated by the receiver operating characteristic curve, area under the curve, which was 0.83. Standard deviations from the probability mean were used to set landslide-susceptibility classifications: low (0–0.10), low–moderate (0.11–0.27), moderate (0.28–0.44), moderate–high (0.45–0.61) and high (0.62–1.0). Logistic-regression results were validated by using a separate landslide inventory for the neighbouring Prestonsburg 7.5-minute quadrangle, and running the same regression function. Results indicate that 74.9% of the landslide deposits were identified as having moderate, moderate–high or high landslide susceptibility. Combining inventory mapping with statistical modelling identified important geomorphic variables and produced a useful approach to landslide-susceptibility mapping. Supplementary material: The statistical data used in the combined machine-learning functions are available at https://doi.org/10.6084/m9.figshare.c.5351313.v3 Thematic collection: This article is part of the Digitization and Digitalization in engineering geology and hydrogeology collection available at: https://www.lyellcollection.org/cc/digitization-and-digitalization-in-engineering-geology-and-hydrogeology

Landslide susceptibility mapping using information value method in Jhelum Valley of the Himalayas

The geological and geographical conditions have made the Himalayas one of the regions most vulnerable to disasters. Northern Pakistan is made up of high-relief Himalayan mountains where landslides are the most frequently occurring catastrophic events. Floods, earthquakes, and landslides occur most often in this region, causing deaths and severe infrastructure damages. The studied area is experiencing significant landslide events triggered by destructive seismic activity, extreme seasonal rainfalls, and human activities. In this study, landslide susceptibility mapping is proposed using an integrated model of information value method by optimizing its capabilities and overcoming its limitations to enhance both precision and accuracy of the results. This approach quantifies and estimates the probability of landslide occurrence using statistical principles and techniques. Seven distinct landslide causative factors, including slope angle, distance to stream, drainage density, distance to road, land cover, normalized difference vegetation index, lithology, and distance to faults were evaluated to determine the areas at risk for future slope movements. A reliable landside inventory from a spatial database combined with ground-truthing field surveys was prepared for evaluating the relationship between the landslides and their causative factors. The accuracy and efficiency of the developed model are evaluated using sensitivity analysis of the receiver operator curve (ROC). The ROC-validated results showed an accuracy of 0.845 for model prediction rate and an accuracy of 0.837 for model success rate, indicating that the model is reliable to produce landslide susceptibility map. The results showed the reasonable efficiency of the applied method for assessing landslide susceptibility in the study area and applicable to other regions with similar geomorphological conditions.

A One-Class-Classifier-Based Negative Data Generation Method for Rapid Earthquake-Induced Landslide Susceptibility Mapping

Machine learning with extensively labeled training samples (e.g., positive and negative data) has received much attention in terms of addressing earthquake-induced landslide susceptibility mapping (LSM). However, the extensive amount of labeled training data required by machine learning, particularly the precise negative data (i.e., non-landslide area), cannot be easily and efficiently collected. To address this issue, this study presents a one-class-classifier-based negative data generation method for rapid earthquake-induced LSM. First, an incomplete landslide inventory (i.e., positive data) was produced with the aid of change detection using before-and-after satellite images and the Geographic Information System (GIS). Second, a one-class classifier was utilized to compute the probability of landslide occurrence based on the incomplete landslide inventory followed by the negative data generation from the low landslide susceptibility areas. Third, the positive data as well as the generated negative data (i.e., non-landslide) were compounded to train a traditional binary classifier to produce the final LSM. Experimental results suggest that the proposed method is capable of achieving a result that is comparable to methods using the complete landslide inventory, and it displays good correspondence with recent landslide events, making it a suitable method for rapid earthquake-induced LSM. The findings in this study would be useful in regional disaster planning and risk reduction.

Calculation of landslide occurrence probability in Taiwan region under different ground motion conditions

In this study, Bayesian probability method and machine learning model are used to study the real occurrence probability of earthquake-induced landslide risk in Taiwan region. The analyses were based on the 1999 Taiwan Chi-Chi Earthquake, the largest earthquake in the history in this Region in a hundred years, thus can provide better control on the prediction accuracy of the model. This seismic event has detailed and complete seismic landslide inventories identified by polygons, including 9272 seismic landslide records. Taking into account the real earthquake landslide occurrence area, the difference in landslide area and the non-sliding/sliding sample ratios and other factors, a total of 13,656,000 model training samples were selected. We also considered other seismic landslide influencing factors, including elevation, slope, aspect, topographic wetness index, lithology, distance to fault, peak ground acceleration and rainfall. Bayesian probability method and machine learning model were combined to establish the multi-factor influence of earthquake landslide occurrence model. The model is then applied to the whole Taiwan region using different ground motion peak accelerations (from 0.1 g to 1.0 g with 0.1 g intervals) as a triggering factor to complete the real probability of earthquake landslide map in Taiwan under different peak ground accelerations, and the functional relationship between different Peak Ground Acceleration and their predicted area is obtained.

Landslide Susceptibility Zonation With Special Emphasis on Tectonic Features for Occurrence of Landslides in Lower Indian Himalaya

Landslide susceptibility zonation (LSZ) map is prepared for a part of the lower Himalayas using the fuzzy cosine amplitude method. The prepared LSZ map is validated both statistically and physically, and it is found that around 30% of the study area lies in zones of high to very high landslide susceptibility. To correlate the Himalayan seismicity with the landslide hazard, the prepared LSZ map is further assessed in terms of the impact of fault distance on the occurrence and spatial distribution of landslides in the study area. The results quantify a strong inverse correlation between fault distance and the probability of landslide occurrence. A high coefficient of determination (R2 = 0.986) indicates that the equation can realistically predict the impact of fault distance on the study area's landslide occurrence. Further, landslide relative frequency (LRF) method is used to evaluate the statistical contribution of fault distance toward overall landslide susceptibility for the study area. It is found that distance from any major tectonic feature is a significant landslide causative parameter, with a relative importance of 21%. The prepared LSZ map will be valuable for decision-makers during smart town planning and sustainable development in the study area.

Susceptibility factors of drainage basins to shallow landslides in coffee-growing areas in the Department of Caldas, Colombia

Due to its geo-environmental complexity and its climatic conditions, the coffee-growing region of the Department of Caldas, Colombia is affected by shallow landslides triggered by rainfall. With the objective of characterizing the factors affecting the susceptibility to mass movements in regional drainage basins and to contribute to an understanding of the susceptibility of landslides in coffee-growing regions, the present study was conducted in the Mica Basin of the Municipality of Pacora, Colombia, at 5°32′28.14′′ N, 75°29′15.35′′ W. With the support of the GIS platform, the inventory of landslides was carried out using orthophotographs and Light Detection and Ranging (LiDAR) information from 2014 at a 10 m × 10 m resolution. This inventory was complemented with field surveys that included susceptibility factors and the influence of the roads. The predisposing factors to landslides were studied using frequency ratio (FR) analysis and multiple logistic regression (MLR) analysis. The FR analysis showed a close relationship among landslides (FR index > 1) with a slope between 27° and 65°, an altitude between 2100 m and 2420 m, a distance to natural drainages between 0 and 40 m, a distance to geological faults between 0 and 200 m, a distance to roads between 20 and 400 m, basic and ultrabasic geological units, soil compounds derived from volcanic ash, and plane curvatures of concave and convex types. The MLR model showed a maximum likelihood ratio of certain factors, such as geology, relief forms, slope angle, altitude, flow direction, plane curvature, distance to natural drainage, distance to faults, and distance to roads, with the probability of landslide occurrence.

The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

The purpose of this study is to establish the criteria for a landslide early warning system (LEWS). We accomplished this by deriving optimal thresholds for the cumulative event rainfall–duration (ED) and identifying the characteristics of the rainfall variables associated with a high probability of landslide occurrence via a Bayesian model. We have established these system criteria using rainfall and landslide data for Chuncheon, Republic of Korea. Heavy rainfall is the leading cause of landslides in Chuncheon; thus, it is crucial to determine the rainfall conditions that trigger landslides. Hourly rainfall data spanning 1999 to 2017 from seven gauging stations were utilized to establish the ED thresholds and the Bayesian model. We used three different calibration periods of rainfall events split by 12, 24, 48, and 96 non-rainfall hours to calibrate the ED thresholds. Finally, the optimal threshold was determined by comparing the results of the contingency table and the skill scores that maximize the probability of detection (POD) score and minimize the probability of false detection (POFD) score. In the LEWS, by considering the first level as “normal,” we developed subsequent step-by-step warning levels based on the Bayesian model as well as the ED thresholds. We propose the second level, “watch,” when the rainfall condition is above the ED thresholds. We then adopt the third level, “warning,” and the fourth level, “severe warning,” based on the probability of landslide occurrence determined via a Bayesian model that considers several factors including the rainfall conditions of landslide vs. non-landslide and various rainfall variables such as hourly maximum rainfall and 3-day antecedent rainfall conditions. The proposed alert level predicted a total of 98.2% of the landslide occurrences at the levels of “severe warning” and “warning” as a result of the model fitness verification. The false alarm rate is 0% for the severe warning level and 47.4% for the warning level. We propose using the optimal ED thresholds to forecast when landslides are likely to occur in the local region. Additionally, we propose the ranges of rainfall variables that represent a high landslide probability based on the Bayesian model to set the landslide warning standard that fits the local area’s characteristics.

Study of qualitative stability analysis and rainfall thresholds for possible landslide occurrence: a case study of Sikkim Himalaya

Landslide hazard characterization in the Setijhora landslide, located near Kalijhora village, along National Highway 10, was carried out in a qualitative way using Rock Mass Rating (RMR), Continuous Slope Mass Rating (CSMR) and Geological Strength Index (GSI). CSMR data helped in the understanding of slope failures (wedge, planner, rotational and toppling), while RMR & GSI data provides a better understanding of rock mass quality and its deformation. RMR was observed between 10–49 in the dry season and 1–39 in the monsoon season. Kinematic/stereographic analyses indicate vulnerability to wedge failure, while CSMR ranges from 13.84 to 50.85 (class III to class V). Rainfall threshold data shows cumulative rainfall of more than 111.3 mm over 20 days and 222 mm in a month indicating a high probability of landslides occurrence. Data generated from the study will be very useful for developing landslide early warning in the study area.

Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong

Landslide susceptibility mapping (LSM) is an effective way to predict spatial probability of landslide occurrence. Existing convolutional neural network (CNN)-based methods apply self-built CNN with simple structure, which failed to reach CNN's full potential on high-level feature extraction, meanwhile ignored the use of numerical predisposing factors. For the purpose of exploring feature fusion based CNN models with greater reliability in LSM, this study proposes an ensemble model based on channel-expanded pre-trained CNN and traditional machine learning model (CPCNN-ML). In CPCNN-ML, pre-trained CNN with mature structure is modified to excavate high-level features of multichannel predisposing factor layers. LSM result is generated by traditional machine learning (ML) model based on hybrid feature of high-level features and numerical predisposing factors. Lantau Island, Hong Kong is selected as study area; temporal landslide inventory is used for model training and evaluation. Experimental results show that CPCNN-ML has ability to predict landslide occurrence with high reliability, especially the CPCNN-ML based on random forest. Contrast experiments with self-built CNN and traditional ML models further embody the superiority of CPCNN-ML. It is worth noting that coastal regions are newly identified landslide-prone regions compared with previous research. This finding is of great reference value for Hong Kong authorities to formulate appropriate disaster prevention and mitigation policies.

Threshold rainfall ranges for landslide occurrence in Matara district of Sri Lanka and findings on community emergency preparedness

The objective of this research was to identify the effects of antecedent rainfall on the probability of landslide occurrence and to analyze the knowledge of the community on Emergency Preparedness.In this study, recent major landslides events were studied. The rainfall relevant to landslide locations was extracted using a GIS-based interpolation.From the daily rainfall of two days prior to landslide event (i.e. Day 3) till the daily rainfall of landslide day (Day 1), there was an identifiable pattern. There is more than 90% chance for a landslide to occur, when the rainfall of Day 1, or the day of the landslide event, is close to 300 mm or above with a rainfall of ~80 mm or above on the previous day (Day 2); i.e. There is more than 94% chance for a landslide to occur when the total rainfall received on Day 1 and Day 2 exceeds 375 mm. The majority of the events (>94%) had a daily rainfall of ~80 mm or above on Day 3. The cumulative rainfall of the landslide day up to three days prior to the landslide event shows a significant pattern (Day 1-Day 4). Overall, when there is a rainfall event of ~80 mm or over, people need to be warned/cautioned about a potential landslide within the next three days, if the rainfall continues to be high.Social survey shows that during 2017, people were more prepared compared to 2003, but still there are many steps to be taken in terms of preparedness.

Landslide susceptibility assessment at Kathmandu Kyirong Highway Corridor in pre-quake, co-seismic and post-quake situations

Kathmandu Kyirong Highway (KKH) is one of the most strategic Sino-Nepal highways. Low-cost mitigation measures are common in Nepalese highways, however, they are not even applied sufficiently to control slope instability since the major part of this highway falls still under the category of feeder road, and thus less resources are made available for its maintenance. It is subjected to frequent landslide events in an annual basis, especially during monsoon season. The Gorkha earthquake, 2015 further mobilized substantial hillslope materials and damaged the road in several locations. The aim of this research is to access the dynamic landslide susceptibility considering pre, co and post seismic mass failures. We mapped 5,349 multi-temporal landslides of 15 years (2004–2018), using high resolution satellite images and field data, and grouped them in aforementioned three time periods. Landslide susceptibility was assessed with the application of ‘certainty factor’ (CF). Seventy percent landslides were used for susceptibility modelling and 30% for validation. The obtained results were evaluated by plotting ‘receiver operative characteristic’ (ROC) curves. The CF performed well with the ‘area under curve’ (AUC) 0.820, 0.875 and 0.817 for the success rates, and 0.809, 0.890 and 0.760 for the prediction rates for respective pre, co and post seismic landslide susceptibility. The accuracy for seismic landslide susceptibility was better than pre and post-quake ones. It might be because of the differences on completeness of the landslide inventory, which might have been possibly done better for the single event based co-seismic landslide mapping in comparison with multitemporal inventories in pre and post-quake situations. The results obtained in this study provide insights on dynamic spatial probability of landslide occurrences in the changing condition of triggering agents. This work can be a good contribution to the methodologies for the evaluation of the dynamic landslide hazard and risk, which will further help to design the efficient mitigation measures along the mountain highways.

Influence of human activity on landslide susceptibility development in the Three Gorges area

Human activities are important factors that trigger frequent occurrences of landslides; thus, for landslide control, it is critical to determine the influence of human activity on landslide occurrence probability. The Three Gorges area is a region in the world that typically experiences serious landslide disasters and frequent human activities. The objective of this work is to employ the Three Gorges area as an example to reveal the impact of human activity on the dynamic development of landslide susceptibility from 2010 to 2019. Some new viewpoints are suggested for the five aspects: (1) High-precision landslide susceptibility maps are generated by a combination of multiresolution segmentation and convolutional neural network algorithms. Moreover, the dynamic development rules of landslide susceptibility from 2010 to 2019 are revealed. (2) The change in landslide susceptibility in the study area from 2010 to 2019 was mainly caused by the combined action of rainfall and human activity. The fluctuation of reservoir water level had a less influence on the development of landslide susceptibility. (3) Some human activities, especially road construction, farmland appropriation for building construction, agricultural reclamation, farmland cultivation and irrigation, initiation of commercial planting, urban expansion, and large-scale deforestation, may dramatically increase landslide occurrence probability. (4) Human activities, e.g., conversion of farmland to forestry, artificial recovery of natural vegetation, and later periods of artificial planting, may obviously reduce landslide susceptibility. (5) The human activity causes and mechanisms influencing landslide susceptibility in the study area are proposed, including transpiration and anchorage of plants, slope reinforcement by plant roots, destruction of slope stress equilibrium, and soil erosion.

Economic landslide susceptibility under a\xa0socio-economic perspective: an application to Umbria Region (Central Italy)

An accurate understanding of physical and socio-economic effects of landslides is fundamental to develop more refined risk management, mitigation strategies and land use policies. In this paper we develop a measure to consider the interconnection between physical and economic exposure, i.e. what we call the economic landslide susceptibility, namely the probability of landslide occurrence in an area weighted for its socio-economic exposure considering the real-estate market values. The economic landslide susceptibility is estimated through a method designed for large areas that we applied to the Umbria Region (Central Italy). The method makes use of landslide susceptibility maps and a real estate market value map for any given area under analysis. The innovative concept of economic landslide susceptibility (that is de facto an ex ante landslide cost assessment) may be interpreted as the potential loss that an area might suffer in terms of its propensity for landslides. Useful applications of the proposed method lie in a better territorial management and in the land use planning.

Temporal prediction modeling for rainfall-induced shallow landslide hazards using extreme value distribution

As the frequency and intensity of heavy rainfall increase, the frequency of extreme rainfall-induced landslides also increases. Thus, the importance of accurate assessment of extreme rainfall-induced landslide hazard increases. Landslide hazard assessment requires estimations of two components: spatial probability and temporal probability. While various approaches have been successfully used to estimate spatial landslide susceptibility, fewer studies have addressed temporal probability and, consequently, a commonly accepted method does not exist. Prior approaches have estimated temporal probability using frequency analysis of past landslides or landslide triggering rainfall events. Hence, a large amount of information was required: sufficiently complete historical data on recurrent landslides and repetitive rainfall events. However, in many cases, it is difficult to obtain such complete historical data. Therefore, this study developed a new approach that can be applied to an area where incomplete data are available or where nonrepetitive landslide events have occurred. To evaluate the temporal probability of landslide occurrence, the developed approach adopted extreme value analysis using the Gumbel distribution. The exceedance probability of a rainfall threshold was evaluated, using the Gumbel model, with 72-h antecedent rainfall threshold. This probability was then considered to be the temporal probability of landslide occurrence. The temporal probability of landslides was then integrated with landslide susceptibility results from a multi-layer perceptron model. Consequently, the landslide hazards for different future time periods, from 1 to 200 years, were estimated.

ALICE (Assessment of Landslides Induced by Climatic Events): A single tool to integrate shallow and deep landslides for susceptibility and hazard assessment

The increasing evolution of urban areas in landslide-prone regions and the modification of precipitation due to global climate change have led to a growing need for landslide hazard assessment (LHA) for local land management and security services. LHA is based on the definition of the spatial and temporal probability of landslide occurrences and is split into three steps: (i) a phenomenon inventory, (ii) a landslide susceptibility analysis, and (iii) a landslide hazard analysis. Nevertheless, in practice, essential information is often missing, and LHA corresponds most of the time to landslide susceptibility assessment acquired by expert knowledge. This article presents a physical-based model (ALICE – Assessment of Landslides Induced by Climatic Events) that can integrate different types of landslides (shallow and deep) with different sizes. This tool can be used at the meso or broader scale (1:25,000 to 1:10,000) of work according to expert knowledge. However, a calibration must be carried out to fit observations and to take uncertainties into account. Thus, a specific strategy is applied to the model on a study site located in the French South Alps where numerous different landslide types were observed and analyzed. After calibration on a small representative area, the best-fitted parameters are applied to a larger area for different types of landslides. Three landslide susceptibility maps (shallow translational landslides, rotational landslides and complex landslides) are produced, and the different susceptibility classes are compared with a landslide susceptibility map produced by a geomorphological approach. The different susceptibility envelopes computed by the model are well integrated into the high-susceptibility envelopes defined by the so-called expert geomorphological approach. Thus, the tool makes it possible to improve the mapping of potentially unstable areas by taking into account parameters, such as the groundwater level in different surficial formations or the geotechnical values of landslide-prone formations, that experts are not always able to integrate. ALICE is therefore proven to be a useful tool for mapping potential landslide-prone areas if the tool is well calibrated, taking into account the expert vision of the field, different landslide types, and different triggering factors, including rainfall and seismic acceleration.

A Landslide Probability Model Based on a Long-Term Landslide Inventory and Rainfall Factors

The prediction and advanced warning of landslide hazards in large-scale areas must deal with a large amount of uncertainty, therefore a growing number of studies are using stochastic models to analyze the probability of landslide occurrences. In this study, we used a modified Thiessen’s polygon method to divide the research area into several rain gauge control areas, and divided the control areas into slope units reflecting the topographic characteristics to enhance the spatial resolution of a landslide probability model. We used a 2000–2015 long-term landslide inventory, daily rainfall, and effective accumulated rainfall to estimate the rainfall threshold that can trigger landslides. We then employed a Poisson probability model and historical rainfall data from 1987 to 2016 to calculate the exceedance probability that rainfall events will exceed the threshold value. We calculated the number of landslides occurring from the events when rainfall exceeds the threshold value in the slope units to estimate the probability that a landslide will occur in this situation. Lastly, we employed the concept of conditional probability by multiplying this probability with the exceedance probability of rainfall events exceeding the threshold value, which yielded the probability that a landslide will occur in each slope unit for one year. The results indicated the slope units with high probability that at least one rainfall event will exceed the threshold value at the same time that one landslide will occur within any one year are largely located in the southwestern part of the Taipei Water Source Domain, and the highest probability is 0.26. These slope units are located in parts of the study area with relatively weak lithology, high elevations, and steep slopes. Compared with probability models based solely on landslide inventories, our proposed landslide probability model, combined with a long-term landslide inventory and rainfall factors, can avoid problems resulting from an incomplete landslide inventory, and can also be used to estimate landslide occurrence probability based on future potential changes in rainfall.

Analysing post-earthquake landslide susceptibility using multi-temporal landslide inventories — a case study in Miansi Town of China

The earthquake that occurred on May 12, 2008, in Wenchuan County aroused a great deal of research on co-seismic landslide susceptibility assessment, but there is still a lack of an evaluation method that considers the activity state of the landslide itself. Therefore, this paper establishes a new susceptibility evaluation model that superimposes the active landslide state based on previous susceptibility evaluation models. Based on a multi-phase landslide database, the probabilistic approach was used to evaluate landslide susceptibility in the Miansi town over many years. We chose the elevation, slope, aspect, and distance from the channel as trigger factors and then used the probability comprehensive discrimination method to calculate the probability of landslide occurrence. Then, the susceptibility results of each period were calculated by superposition with the activity rate. The results show that between 2008 and 2014, the proportion of areas with low landslide susceptibility in the study area was the largest, and the proportion of areas with the highest susceptibility was minimal. The landslide area with highest susceptibility gradually decreased from 2014 to 2017. However, in 2017, 15.06% of the area was still with high susceptibility, and relevant disaster prevention and reduction measures should be taken in these areas. The larger area under the receiver operating characteristic curve (AUC) indicates that the results of the landslide susceptibility assessment in this study are more objective and reliable than those of previous models. The difference in the AUC values over many years shows that the accuracy of the evaluation results of this model is not constant, and a greater number of landslides or higher landslide activity corresponds to a higher accuracy of the evaluation results.

Landslide Distribution and Processes in the Hills of Central Nepal: Geomorphic and Statistical Approach to Susceptibility Assessment

The study examined the landslide distribution, processes, and susceptibility of the Lalbakaiya watershed using GIS and remote sensing technology. Inventory of landslides was done using high-resolution satellite imagery available on Google Earth and was verified and further investigated during the field visit. Geomorphic as well as statistical approaches were applied to assess landslides susceptibility and the significance of their outputs was discussed. Map layers representing conditioning and triggering factors of landslide occurrence were produced from various spatial data sources. The study found that the landslide of the Lalbakaiya watershed is primarily controlled by geology representing young, weak, fragile, and weathered sedimentary rocks. Besides, the role of topography such as steep slope, high relative relief, and land use and land cover played an important role in determining the landslide susceptibility. These processes are triggered by monsoon precipitation, seismicity, and land use change in addition to other factors. The geomorphic approach produces a reliable landslide susceptible map as evidenced by past and present (active) failures on a landscape unit, but this map has low predictability of the landslides occurrence. In contrast, the landslide susceptibility map derived from the landslide index method fairly conforms with that derived from the geomorphic approach. Susceptibility calculated by landslide index map is represented by a pixel value that indicates a probability of landslides occurrence, and is amenable to group into various susceptible classes. The model can predict areas of landslides based on quantitative relation between landslides and geo-ecological factors. The limitation of this approach is that these susceptible areas do not represent clearly defined landscape units, and can also overlook highly erodible areas where landslides are not apparent despite severe erosion and numerous minor failures. The study confirms that both geomorphic and statistical approaches can be complementarily integrated to produce predictable, reliable, and applicable landslide susceptibility maps that can make a plausible planning tool for conservation, development, and disaster risk reduction in the populated slopes of the Himalayas and like.

Probabilistic Rainfall Thresholds for Shallow Landslides Initiation – A Case Study from the Nilgiris District, Western Ghats, India

Rainfall is one of the major causes of landslides/landslips across the globe. The fatalities and damage caused by rainfall induced landslides increased in recent days. The Nilgiris district in Western Ghats part of Tamil Nadu state is one of the very high to severe landslide hazard-prone areas of India. The present study is focused on estimation of rainfall thresholds and the temporal probability of landslides in different landslide-prone slopes in part of The Nilgiris district. The landslide prone areas identified in earlier research are used for the present study. The landslide locations data for the years 1824 to 2018 were collected and a spatial database on landslide inventory was created. A detailed inventory carried out on the 2009 landslides were analysed and used for the calculation of rainfall thresholds. Monthly and Yearly Rainfall data for the years 2000 to 2011 were collected for 37 rainguage stations from various government agencies. Based on the quality and quantity of data, the rainfall thresholds for 14 different locations were estimated viz., Aderly, Coonoor, Coonoor Railway, Governor Sola, Ooty (Near Botanical Garden), Runnymedu, Burliar where the probability of landslide occurrences is high. The temporal probability of landslide was calculated for four years viz., 1, 3, 5 and 10 years. The present study can be used as a key to develop an early warning system in The Nilgiris District.