Probability Evaluation Study of Soil Erosion Types in the Songhua River Basin Based on Deep Learning and Gaussian Kernel Density Estimation

Abstract

[Objective] To scientifically identify the types of soil erosion at the watershed scale and give the corresponding probability of occurrence. [Methods] This study constructs a deep learning (DL)-based model for calculating soil erosion modulus in the Songhua River Basin and calculates different types of soil erosion modulus. Using three erosion modulus influencing factors, namely rainfall, air temperature and wind speed, as random variables, numerical simulation and Gaussian Kernel Density Estimation (GKDE) were used to construct the EM probability evaluation method, which gives the probability of occurrence of different combinations of soil erosion intensities. [Results] The R2 of the validation period of the EM computational models were all >0.86; 74.47% of the average annual occurrence of slight water erosion and slight wind erosion in the watershed; 12.86% of the area of slight and above water erosion and slight wind erosion; 12.56% of the area of slight and above wind erosion and slight water erosion; 0.11% of the area of water erosion strength and wind erosion intensity are both slight and above. 36 typical image elements of the 36 typical images, the average probability of occurrence of slight water erosion and slight wind erosion is 57.45%; the average probability of occurrence of slight water erosion and mild wind erosion is 30.26%; the average probability of occurrence of slight water erosion and moderate wind erosion is 8.03%; the average probability of occurrence of mild water erosion and slight wind erosion is 2.11%; the average probability of occurrence of slight water erosion and severe wind erosion is 2.08%; and the evaluations of occurrence of the remaining combinations of probabilities were all below 0.05%. [Conclusion] The calculation model of erosion modulus in the Songhua River basin constructed in this study has high accuracy, reveals the spatial distribution characteristics of soil erosion types in the Songhua River basin, and gives the probability of occurrence of different combinations of the intensity of the two types of erosion, which provides a basis for the management of soil erosion in the Songhua River basin.

Keywords

• songhua river basin
• soil erosion
• deep learning
• gaussian kernel density estimation method
• probability evaluation