Winter–Spring Prediction of Snow Avalanche Susceptibility Using Optimisation Multi-Source Heterogeneous Factors in the Western Tianshan Mountains, China

Jinming Yang,Yang Liu,Qing He

doi:10.3390/rs14061340

Abstract

Data-driven methods are commonly applied in avalanche hazard evaluation. However, few studies have tapped into the relationship between the explanatory variables and avalanche hazard in arid–frigid areas, and the seasonal dynamics of avalanche hazard and its attribution has not been discussed. Therefore, to fill the gap in the hazard assessment of a dry–cold snow avalanche, quantify the dynamic driving process of seasonal nonlinear explanatory variables on avalanche hazard, and improve the reliability of the assessments, this study used Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbour (KNN) algorithms to construct three assessment models; these were used and verified in the western Tianshan Mountains, China. The following results were obtained: The causative factors of avalanches varied based on the season. In winter, terrain and snow depth played a major role, whereas spring was mainly influenced by snow depth and meteorological factors. The dynamic process of avalanche hazard was mainly governed by the seasonality of snow depth and temperature. The seasonal changes in avalanche hazard increased from low to high. The performance of all models was consistent for season and more reliable than the inter-annual evaluations. Among them, the RF model had the best prediction accuracy, with AUC values of 0.88, 0.91 and 0.78 in winter, spring and the control group, respectively. The overall accuracy of the model with multi-source heterogeneous factors was 0.212–0.444 higher than that of exclusive terrain factors. In general, the optimised model could accurately describe the complex nonlinear collaborative relationship between avalanche hazard and its explanatory variables, coupled with a more accurate evaluation. Moreover, free from inter-annual scale, the seasonal avalanche hazard assessment tweaked the model to the best performance.

Highlights

Based on the above-mentioned considerations, this study aimed to establish a framework for assessing the avalanche hazard of dry–cold snow in the western Tianshan Mountains of China
The results show that when the performance of the Support Vector Machine (SVM) model for winter, spring and the control group is good, the area under curve (AUC) value can reach 0.995
The results reported in those studies show that the SVM, Random Forest (RF) and K-Nearest Neighbour (KNN) algorithms are suitable for evaluating avalanche hazard in high and cold mountain areas, and they highlight the advantages of a data-driven machine learning model in avalanche hazard assessment

Summary

Introduction

Snow avalanches are sudden and destructive natural disasters in mountainous regions of high altitude and low temperature [1,2]. They can fall at a speed of more than 200 km/h, with a pressure of up to 50 t/m2 [3]. Hazard modelling and sensitivity mapping in avalancheprone regions are very important for hazard management, as well as being crucial for targeted disaster adaptation and mitigation. Combined with avalanche dynamics and depending on the changes in the characteristics of the snow, such as the gradient changes in temperature, density and moisture content of the snow layer, the physical model evaluates the snow stability and assesses the avalanche sensitivity [7]. By quantifying the complex effects of weather on the characteristics of snow cover under specific terrain conditions, the Safran–Crocus–Mepra

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Methods

Discussion

Conclusion