Landslide susceptibility assessment in the Anfu County, China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND)

The present study is aimed at producing landslide susceptibility map of a landslide-prone area (Anfu County, China) by using evidential belief function (EBF), frequency ratio (FR) and Mahalanobis distance (MD) models. To this aim, 302 landslides were mapped based on earlier reports and aerial photographs, as well as, carrying out several field surveys. The landslide inventory was randomly split into a training dataset (70%; 212landslides) for training the models and the remaining (30%; 90 landslides) was cast off for validation purpose. A total of sixteen geo-environmental conditioning factors were considered as inputs to the models: slope degree, slope aspect, plan curvature, profile curvature, the new topo-hydrological factor termed height above the nearest drainage (HAND), average annual rainfall, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), soil texture, and land use/cover. The validation of susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC). As a results, the FR outperformed other models with an AUROC of 84.98%, followed by EBF (78.63%) and MD (78.50%) models. The percentage of susceptibility classes for each model revealed that MD model managed to build a compendious map focused at highly susceptible areas (high and very high classes) with an overall area of approximately 17%, followed by FR (22.76%) and EBF (31%). The premier model (FR) attested that the five factors mostly influenced the landslide occurrence in the area: NDVI, soil texture, slope degree, altitude, and HAND. Interestingly, HAND could manifest clearer pattern with regard to landslide occurrence compared to other topo-hydrological factors such as SPI, STI, and distance to rivers. Lastly, it can be conceived that the susceptibility of the area to landsliding is more subjected to a complex environmental set of factors rather than anthropological ones (residential areas and distance to roads). This upshot can make a platform for further pragmatic measures regarding hazard-planning actions

Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods

The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of entropy (IOE), weighted linear combination (WLC)] and machine learning [support vector machine (SVM)] models for estimating landslide susceptibility at the Wuning area, China. A total of 445 landslides were randomly classified into 70% and 30% to train and validate landslide models, respectively. Fourteen landslide conditioning factors including slope angle, slope aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, NDVI, land use, rainfall, distance to road, distance to river and distance to fault were then studied for landslide susceptibility assessment. Performances of five studied models were evaluated using area under the ROC curve (AUROC) for training (success rate curve) and validation (prediction rate curve) datasets, statistical-based measures and tests. Results indicated that the area under the success rate curve for the FR, IOE, WLC, SVM and SMCE models was 88.32%, 82.58%, 78.91%, 85.47% and 89.96%, respectively, demonstrating that SMCE could provide the higher accuracy. The prediction capability findings revealed that the SMCE model (AUC = 86.81%) was also the highest approach among the five studied models, followed by the FR (AUC = 84.53%), the SVM (AUC = 81.24%), the IOE (AUC = 79.67%) and WLC (73.92%) methods. The landslide susceptibility maps derived from the above five models are reasonably accurate and could be used to perform elementary land use planning for hazard extenuation