Mining soil heavy metal inversion based on Levy Flight Cauchy Gaussian perturbation sparrow search algorithm support vector regression (LSSA-SVR)

Abstract

Soil heavy metal pollution in mining areas poses severe challenges to the ecological environment. In recent years, machine learning has been widely used in heavy metal inversion by hyperspectral data. However, deterministic algorithms and probabilistic algorithms may confront local optimal solutions in practical applications. The local optimal solution is not the optimal value obtained within the entire defined interval, and as a result will affect the reliability of these approaches. This paper proposes a Levy Flight Cauchy Gaussian perturbation Sparrow Search algorithm Support Vector Regression (LSSA-SVR) soil heavy metal content prediction model. It introduces Levy Flight (LF) measurement and Cauchy Gaussian perturbation based on the Sparrow search algorithm. The LSSA-SVR model was shown to increase the breadth of solutions searched, avoiding the local optimal solution problem. When applied to mining soil heavy metal experiments, we found that the LSSA-SVR model gave a good fit for the elements Cu, Zn, As, and Pb. The correlation coefficients between the predicted results and the actual results of the four elements were all above 0.94. The heavy metal predicted results of LSSA-SVR have a small error margin in both the overall distribution and in individual differences. This study provides an efficient and accurate monitoring method for mining soil heavy metal inversion. It also provides strong support for environmental management and soil remediation.