Landslide Identification and Zonation Using the Index of Entropy Technique at Ossey Watershed Area in Bhutan

The landslide is one of the natural disasters which claim human lives and incur huge economic losses, especially in the mountainous area. The main aim of this study is to develop different zones of landslide-prone area using the index of entropy (IOE) at the Ossey watershed area in Bhutan. During the landslide inventory, 164 landslides were identified of which 115 locations were used for the training dataset while the remaining 49 locations were used for the validation dataset. A total of ten causal factors were used for this study including elevation, slope, aspect, slope curvature, stream power index, normalized difference vegetation index (NDVI), distance from the road, distance from the river, lithology, and rainfall. The IOE was used to obtain the relationship between the landslide events and the causal factors. The most influential causal factors were NDVI, slope, and rainfall with the weightage of 0.377, 0.347, and 0.175 respectively as per the IOE. The final landslide susceptibility map was classified into five classes using the geometrical interval classification. The validation was done using the receiver operating characteristic (ROC) curves and the kappa index. The area under the curve (AUC) for the success rate and prediction rate was 0.7821 and 0.8377, respectively. The kappa index using the training dataset and validation dataset were 0.4111 and 0.4898, respectively. The final landslide susceptibility map is accurate enough for the future references by the decision-makers and the engineers

Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms

AbstractLandslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage $${(\mathrm{Ds}}_{\mathrm{d}})$$ ( Ds d ) , distance to faults $${(\mathrm{Ds}}_{\mathrm{f}})$$ ( Ds f ) , drainage density ($${D}_{d})$$ D d ) , elevation (Elev), fault density $$({F}_{d})$$ ( F d ) , lithology, normalized difference vegetation index (NDVI), plan curvature $${(\mathrm{Pl}}_{\mathrm{c}})$$ ( Pl c ) , profile curvature $${(\mathrm{Pr}}_{\mathrm{c}})$$ ( Pr c ) , slope $${(S}^{^\circ })$$ ( S ∘ ) , stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions ( 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. Graphical Abstrac

Landslide Susceptibility Mapping Using Machine Learning:A Danish Case Study

Mapping of landslides, conducted in 2021 by the Geological Survey of Denmark and Greenland (GEUS), revealed 3202 landslides in Denmark, indicating that they might pose a bigger problem than previously acknowledged. Moreover, the changing climate is assumed to have an impact on landslide occurrences in the future. The aim of this study is to conduct the first landslide susceptibility mapping (LSM) in Denmark, reducing the geographical bias existing in LSM studies, and to identify areas prone to landslides in the future following representative concentration pathway RCP8.5, based on a set of explanatory variables in an area of interest located around Vejle Fjord, Jutland, Denmark. A subset from the landslide inventory provided by GEUS is used as ground truth data. Three well-established machine learning (ML) algorithms—Random Forest, Support Vector Machine, and Logistic Regression—were trained to classify the data samples as landslide or non-landslide, treating the ML task as a binary classification and expressing the results in the form of a probability in order to produce susceptibility maps. The classification results were validated through the test data and through an external data set for an area located outside of the region of interest. While the high predictive performance varied slightly among the three models on the test data, the LR and SVM demonstrated inferior accuracy outside of the study area. The results show that the RF model has robustness and potential for applicability in landslide susceptibility mapping in low-lying landscapes of Denmark in the present. The conducted mapping can become a step forward towards planning for mitigative and protective measures in landslide-prone areas in Denmark, providing policy-makers with necessary decision support. However, the map of the future climate change scenario shows the reduction of the susceptible areas, raising the question of the choice of the climate models and variables in the analysis

Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps

Landslides are prevalent in the Western Ghats, and the incidences that happened in 2021 in the Koottickal area of the Kottayam district (Western Ghats) resulted in the loss of 10 lives. The objectives of this study are to assess the landslide susceptibility of the high-range local self-governments (LSGs) in the Kottayam district using the analytical hierarchy process (AHP) and fuzzy-AHP (F-AHP) models and to compare the performance of existing landslide susceptible maps. This area never witnessed any massive landslides of this dimension, which warrants the necessity of relooking into the existing landslide-susceptible models. For AHP and F-AHP modeling, ten conditioning factors were selected: slope, soil texture, land use/land cover (LULC), geomorphology, road buffer, lithology, and satellite image-derived indices such as the normalized difference road landslide index (NDRLI), the normalized difference water index (NDWI), the normalized burn ratio (NBR), and the soil-adjusted vegetation index (SAVI). The landslide-susceptible zones were categorized into three: low, moderate, and high. The validation of the maps created using the receiver operating characteristic (ROC) technique ascertained the performances of the AHP, F-AHP, and TISSA maps as excellent, with an area under the ROC curve (AUC) value above 0.80, and the NCESS map as acceptable, with an AUC value above 0.70. Though the difference is negligible, the map prepared using the TISSA model has better performance (AUC = 0.889) than the F-AHP (AUC = 0.872), AHP (AUC = 0.867), and NCESS (AUC = 0.789) models. The validation of maps employing other matrices such as accuracy, mean absolute error (MAE), and root mean square error (RMSE) also confirmed that the TISSA model (0.869, 0.226, and 0.122, respectively) has better performance, followed by the F-AHP (0.856, 0.243, and 0.147, respectively), AHP (0.855, 0.249, and 0.159, respectively), and NCESS (0.770, 0.309, and 0.177, respectively) models. The most landslide-inducing factors in this area that were identified through this study are slope, soil texture, LULC, geomorphology, and NDRLI. Koottickal, Poonjar-Thekkekara, Moonnilavu, Thalanad, and Koruthodu are the LSGs that are highly susceptible to landslides. The identification of landslide-susceptible areas using diversified techniques will aid decision-makers in identifying critical infrastructure at risk and alternate routes for emergency evacuation of people to safer terrain during an exigency

Susceptibilidad de deslizamientos a escala regional. Análisis y visualización de resultados orientados a la gestión del territorio

La susceptibilitat d'esllavissades pot ser interpretada com una tendència d'una regió a generar esllavissades (Guzzetti et al., 2006). Atès que aquests fenòmens a escala regional poden ser desencadenats per diversos factors usualment no disponibles a aquesta magnitud. Es comú emprar-se en les anàlisis d'esllavissades en models digitals d'elevació i, en alguns casos, amb base a la informació d'usos del terreny i mapes geològics (Palau et al., 2020). L'objectiu principal d'aquest estudi és estimar la probabilitat de ruptura a escala regional en base a dades georeferenciades i generar un mapa de susceptibilitat de lliscament tenint com a unitats de terreny, les unitats hidrològiques. D'altra banda, es proposen i s'avaluen diversos criteris d'agrupació de les classes de susceptibilitat. Es van generar mapes de conques i mitjanes conques hidrogràfiques tenint com a dada d'entrada el model digital d'elevació amb diferents valors de nombre mínim de píxels. Aquest paràmetre es va mostrar molt important, vist que afecta a les àrees dels polígons en la seva distribució, en el mapa i en la seva morfologia. Es van estudiar cinc possibles valors de nombre mínim de píxels iguals a 1 milió, 750, 500, 250 i 100 mil. Els resultats van mostrar que es té una distribució més equitativa d'àrees amb les conques amb menors espais mínims. Es van estudiar vuit criteris d'agrupació: tres criteris estadístics i cinc criteris proposats en aquest estudi denominats de "A" a "I". El criteri "C" va proporcionar el resultat més satisfactori.La susceptibilidad de deslizamientos puede ser interpretada como una tendencia de una región en generar deslizamientos (Guzzetti et al., 2006). Dado que estos fenómenos a escala regional pueden ser desencadenados por varios factores usualmente no disponibles a esta magnitud, es común emplearse en los análisis de deslizamientos modelos digitales de elevación y, en algunos casos con base en la información de usos del terreno y mapas geológicos (Palau et al., 2020). El objetivo principal de este estudio es estimar la probabilidad de rotura a escala regional con base en datos georreferenciados y generar un mapa de susceptibilidad de deslizamiento teniendo como unidades de terreno, las unidades hidrológicas. Por otra parte, se proponen y evalúan diversos criterios de agrupación de las clases de susceptibilidad. Se generaron mapas de cuencas y medias cuencas hidrográficas teniendo como dato de entrada el modelo digital de elevación con diferentes valores de número mínimo de pixeles. Este parámetro se mostró de suma importancia, visto que afecta las áreas de los polígonos, su distribución en el mapa y su morfología. Se estudiaron cinco posibles valores de número mínimo de pixeles iguales a 1 millón, 750, 500, 250 y 100 mil. Los resultados mostraron que se tiene una distribución más equitativa de áreas con las cuencas con menores tamaños mínimos. Se estudiaron ocho criterios de agrupación: tres criterios estadísticos y otros cinco criterios propuestos en este estudio denominados de “A” a “E”. El criterio “C” proporcionó el resultado más satisfactorio.The susceptibility to landslides can be interpreted as a tendency of a region to generate landslides (Guzzetti et al., 2006). Given that these phenomena on a regional scale can be triggered by various factors usually not available at this magnitude, it is common to use digital elevation models in landslide analysis and, in some cases, based on information from land use and geological maps (Palau et al., 2020). The main objective of this study is to estimate the probability of failure on a regional scale based on georeferenced data and to generate a landslide susceptibility map having hydrological units as terrain units. On the other hand, various criteria for grouping the susceptibility classes are proposed and evaluated. Basins and half basins maps were generated with the digital elevation model as input data with different values of the minimum number of pixels. This parameter was very important, since it affects the areas of the polygons, their distribution on the map and their morphology. Five possible values of the minimum number of pixels equal to 1 million, 750, 500, 250 and 100 thousand were studied. The results showed that there is a more equitable distribution of areas with the basins with smaller minimum sizes. Eight grouping criteria were studied: three statistical and another five criteria proposed in this study called “A” to “E”. Criterion "C" provided the most satisfactory result

Landslide Susceptibility Mapping Using Logistic Regression Analysis along the Jinsha River and Its Tributaries Close to Derong and Deqin County, Southwestern China

The objective of this study was to identify the areas that are most susceptible to landslide occurrence, and to find the key factors associated with landslides along Jinsha River and its tributaries close to Derong and Deqin County. Thirteen influencing factors, including (a) lithology, (b) slope angle, (c) slope aspect, (d) TWI, (e) curvature, (f) SPI, (g) STI, (h) topographic relief, (i) rainfall, (j) vegetation, (k) NDVI, (l) distance-to-river, (m) and distance-to-fault, were selected as the landslide conditioning factors in landslide susceptibility mapping. These factors were mainly obtained from the field survey, digital elevation model (DEM), and Landsat 4⁻5 imagery using ArcGIS software. A total of 40 landslides were identified in the study area from field survey and aerial photos’ interpretation. First, the frequency ratio (FR) method was used to clarify the relationship between the landslide occurrence and the influencing factors. Then, the principal component analysis (PCA) was used to eliminate multiple collinearities between the 13 influencing factors and to reduce the dimension of the influencing factors. Subsequently, the factors that were reselected using the PCA were introduced into the logistic regression analysis to produce the landslide susceptibility map. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the logistic regression analysis model. The landslide susceptibility map was divided into the following five classes: very low, low, moderate, high, and very high. The results showed that the ratios of the areas of the five susceptibility classes were 23.14%, 22.49%, 18.00%, 19.08%, and 17.28%, respectively. And the prediction accuracy of the model was 83.4%. The results were also compared with the FR method (79.9%) and the AHP method (76.9%), which meant that the susceptibility model was reasonable. Finally, the key factors of the landslide occurrence were determined based on the above results. Consequently, this study could serve as an effective guide for further land use planning and for the implementation of development