Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Abstract

Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.

Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model

“…The mIOU is used by [12] to validate the results of landslide detection based on different machine learning models and deep learning approaches. This validation measure is extensively used in image processing and computer vision for object detection cases [64]. The mIOU (see equation 11) is an appropriate measure to validate the results that are in polygon based on an inventory dataset of which is also represent by polygons (see Fig.…”

Earthquake Vulnerability Mapping Using Different Hybrid Models

“…Researchers have previously focused on individual models for vulnerability, assessing the location of natural disasters such as earthquakes. However, many hybrid models have recently been used to model natural hazards [37,40,81]. The purpose of this study was to introduce new hybrid learning models for seismic vulnerability mapping in Sanandaj City.…”

“…Other methods and models have been used to map natural hazard vulnerability in some current studies, including certainty factors (CF) [20], ANN [21,22], logistic regression (LR) [23], support vector machine (SVM) [24][25][26], convolutional neural network (CNN) [27], ordered weight averaging (OWA) [4], fuzzy quantifier algorithm [28], adaptive neuron-fuzzy inference system (ANFIS) [29,30], and different multiple criteria decision analysis (MCDA) models [31] such as the analytic hierarchy process (AHP) [32][33][34] and the analytical network process (ANP) [35,36]. Several models and techniques have also been integrated and combined to produce more efficient hybrid models [37][38][39]. [22] integrated a hazard susceptibility index with a social/infrastructural vulnerability index using a geographic information system multi-criteria decision making (GIS-MCDM).…”

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“…Близкой к задаче выявления оползней является прогноз лавинной опасности: геометрия склонов во многом контролирует накопление снежных масс, а сход лавины можно рассматривать как гравитационный склоновый процесс. В этой связи методы выявления лавиноопасных участков с использованием морфометрических моделей сходны с рассмотренными выше подходами к выявлению и прогнозному картографированию оползней [Veitinger et al, 2016;Bühler et al, 2018;Rahmati et al, 2019].…”