Institutional mapping and causal analysis of avalanche vulnerable areas based on multi-source data

TL;DR

This paper combines multi-source remote sensing data, neural networks, and statistical analysis to map avalanche-prone areas and improve prediction accuracy, aiding safety and infrastructure planning in Southwest China.

Contribution

It introduces a novel integration of multi-source remote sensing, deep learning, and statistical models for long-term avalanche risk mapping and prediction.

Findings

Identified avalanche-prone areas using U-net CNN and threshold analysis.

Analyzed the relationship between earthquake magnitude and avalanche distribution.

Produced high-precision avalanche prediction products for Southwest China.

Abstract

Avalanche disaster is a major natural disaster that seriously threatens the national infrastructure and personnel's life safety. For a long time, the research of avalanche disaster prediction in the world is insufficient, there are only some basic models and basic conditions of occurrence, and there is no long series and wide range of avalanche disaster prediction products. Based on 7 different bands and different types of multi-source remote sensing data,this study combined with existing avalanche occurrence models, field investigation and statistical data to analyze the causes of avalanche. The U-net convolutional neural network and threshold analysis were used to extract the distribution of long time series avalanch-prone areas in two study areas, Heiluogou in Sichuan Province and along the Zangpo River in Palong, Tibet Autonomous Region. In addition, the relationship between earthquake magnitude and spatial distribution and avalanche occurrence is also analyzed in this study. This study will also continue to build a prior knowledge base of avalanche occurrence conditions, improve the prediction accuracy of the two methods, and produce products in long time series interannual avalanch-prone areas in southwest China, including Sichuan Province, Yunnan Province, and Tibet Autonomous Region. The resulting products will provide high-precision avalanche prediction and safety assurance for engineering construction and mountaineering activities in Southwest China.