STUDY ON REAL-TIME ESTIMATION METHOD OF SNOW DEPTH DISTRIBUTION BY DEEP LEARNING USING SATELLITE DATA

Abstract

Estimating the amount of accumulated snow in mountains is important in terms of both water use and disaster prevention. However, limited number of observation point makes it difficult to graspe the snow depth distribution accurately and quickly. To address this limitation, we propose a deep learning method that estimates snow depth distribution from information which can be obtained in real-time, such as snow cover extent derived from satellite data and snow depth at observation points. Physical simulations that modeled the snow accumulation / snow melting process were used to create training data such as spatial distribution of snow depth, which has no past actual measurement values, and snow cover extent. The model trained by the data for two years during winter season were able to estimate the tendency of changes in snow depth during the snow accumulation and snowmelt seasons of another year. From the comparison of test cases with different training target, it was confirmed that the snow depth range of the training target affects the tendency inferenced, and adding various data to the training dataset leads to improvement in accuracy.