[en] Few studies have been conducted for susceptibility of rock falls in mountainous areas. In this study, we compare and evaluate rock fall susceptibility mapping using bivariate statistical [weight of evidence (WoE)], analytical hierarchy process (AHP) and frequency ratio (FR) methods along 11 km of a mountainous road in the Salavat Abad saddle in southwestern Kurdistan, Iran. A total of 34 rock fall locations were constructed from various sources. These rock fall locations were then partitioned into a training dataset (70% of the rock fall locations) and a testing dataset (30% of the rock fall locations). Eight conditioning factors affecting on the rock falls including slope angle, aspect, curvature, elevation, distance to road, distance to fault, lithology and land use were identified. The modeling process and rock fall susceptibility mapping has been constructed using three methods. The performance of rock fall susceptibility mapping was evaluated using the area under the curve of success rate curve for training and prediction rate curves (PRC) for testing datasets and also seed cell area index. The results show that the rock fall susceptibility mapping using the WOE method has better prediction accuracy than the AHP and FR methods. Ultimately, the weight-of-evidence method is a promising technique so that it is proposed to manage and mitigate the damages of rock falls in the prone areas.

[en] 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% (311 landslides) and 30% (134 landslides) 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.

[en] Highlights: • Machine learning models namely LMT, REPT, NBT and ADT were used for flood assessment. • Out of four models, the ADT has the highest performance for flood assessment. • Advanced Decision Trees methods are promising for flood assessment in prone areas. Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas.