融合差分进化与粒子群优化的磷化工污染土壤扩散预测方法OA

Although certain progress has been made in high-precision prediction of soil pollutants diffusion,how to effectively integrate multi-dimensional factors in complex environments while balancing global search and local refinement capabilities of parameter optimization remains the key to improving the performance of prediction models.To address the problems of insufficient modeling of multiple environmental impact factors and the tendency of single optimization algorithms to fall into local optima in the prediction of soil pollutants diffusion in the phosphorus chemical industry,a prediction method integrating differential evolution and particle swarm optimization(DE-PSO)was proposed.Multi-dimensional data were obtained through regular sample collection and real-time sensor monitoring,and the sensor data were calibrated using linear regression and the least squares method.A pollutant diffusion mechanism model integrating environmental factors such as soil temperature,humidity,and pH value was constructed,and an alternating iteration mechanism of DE and PSO was designed to collaboratively optimize the key parameters of the model.Results showed that in the diffusion prediction of typical phosphorus chemical industry pollutants such as cadmium and fluoride,the root mean square error(RMSE)of this method was as low as 0.033 9,the coefficient of determination reached 0.971,and the number of convergence iterations was only 70.Both the prediction accuracy and computational efficiency were significantly superior to those of single DE,single PSO,and traditional genetic algorithms.This method provides a new technical path for the high-precision,real-time prediction of soil pollutants diffusion in complex environments.