Snow avalanche hazard prediction using machine learning methods

Abstract

Snow avalanches are among the most destructive natural hazards threatening human life, ecosystems, built structures, and landscapes in mountainous regions. The complexity of snow avalanche modelling has been discussed in many studies, but its modelling is not well-documented. Snow avalanche modeling in this study was done using three main categories of data, including avalanche occurrence locations, meteorological factors, and terrain characteristics. Two machine learning models, namely support vector machine (SVM) and multivariate discriminant analysis (MDA), were employed. A ratio of 70 to 30 of data was considered for calibrating and validating the models. Results indicated that both models had an excellent performance in snow avalanche modeling (area under curve, AUC > 90), although hits and misses analysis demonstrated the superior performance of MDA. Sensitivity analysis indicated that the topographic position index, slope, precipitation, and topographic wetness index were the most effective variables for modeling. A snow avalanche map indicated that the high snow avalanche hazard zone was mostly near the streams and was matched with hillsides around the water pathways. Findings of study can be helpful for land use planning, to control snow avalanche paths, and to prevent the probable hazards induced by it, and it can be a good reference for future studies on modeling snow avalanche hazards.

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

“…Since the performances of RF, NB and GAM in the field of snow avalanche hazard have not been evaluated previously, it is impossible to directly compare these results to previous studies. The accuracy of SVM, however, confirms results achieved by Choubin et al [24]. Numerous studies have applied AHP models which are based on the knowledge and judgments of experts, but the reliance on subjective expert input can be the primary drawback of using them [118].…”

“…Some researchers have used numerical methods and dynamic models to analyze the snow avalanche hazard [18][19][20][21], however a lot of uncertainty is involved in all the modeling parameters when applying to large regions at smaller scales. Other research methods are based upon remote-sensing techniques [14,[22][23][24][25]. Though remote sensing can provide useful information about snow surface and land surface conditions, analysis of the complicated relationships between snow avalanches and geomorphometrics has been often ignored.…”

“…Their results indicated that this simple model performed well in the task of identifying snow avalanche-prone areas. Choubin et al [24] have successfully applied support vector machines (SVM) and multivariate discriminant analysis (MDA) to spatially predict this snow avalanche hazard. Their results demonstrated that both models achieve excellent predictive capacity for snow avalanche mapping.…”

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

“…Among them, machine learning methods have been reported to deliver higher performance in terms of accuracy, robustness, and lower computational power in dealing with uncertainties and big data [38][39][40][41]. Several surveys report that ensemble and hybrid models are the future trends in machine learning due to the fact of their optimized algorithms for higher efficiency [42][43][44][45][46][47][48]. Hybrid machine learning models are shown to deliver higher performance in air pollution modeling and prediction [49][50][51][52][53][54].…”

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

“…Literature includes an adequate number of state of the art review papers and comparative analysis on the general applications of ML and DL methods [22][23][24][25][26][27][28][29][30][31][32][33][34][35]. The trends of the advancement of ML and DL methods are reported to be hybrid and ensemble methods [36][37][38][39][40][41][42][43][44][45][46].…”