Snow Density Prediction Model in Hubei Based on Random Forest and Its Application in Snow Pressure Analysis
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Abstract:
The lack of snow parameters such as snow density and snow pressure is one of the difficulties in the study of snow disaster prevention in the Southern China. It is a helpful supplement to the existing snow monitoring data to invert the snow density of the station and its surroundings through historical ground snow meteorological observation data. Based on the daily meteorological observation data of 76 stations in Hubei Province, this paper analyzes and selects eight independent variable factors affecting snow density, such as the number of snow days, snow depth, air temperature and sunshine, and constructs a Random Forest (RF) regression model of snow density. Through the inversion data of the RF model, the distributions of snow density and snow pressure in Hubei Province are analyzed. The results show that: (1) The root mean square error predicted by the snow density RF model is about 0.04 g/cm3, which can be used for the inversion of snow density data in Hubei Province. (2) The average snow density in Hubei Province is between 0.14 and 0.20 g/cm3, and is divided into east and west regions based on the value of 0.17 g/cm3, with the larger snow density in the eastern region. (3) The maximum snow pressure in Hubei Province in recent 60 years is between 1.3 and 6.7 g/cm2. The distributions of maximum snow pressure in different return periods have two high-value areas in northwest and east of Hubei Province, and the basic snow pressure value in the northcentral part of the east of Hubei Province is greater.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.