Soil Heavy-Metal Pollution Prediction Methods Based on Two Improved Neural Network Models

Abstract

Current soil pollution prediction methods need improvement, especially with regard to accuracy in supplementing missing heavy-metal values in soil, and the accuracy and slow convergence speed of methods for predicting heavy-metal content at unknown points. To reduce costs and improve prediction accuracy, this study used two neural network models (SA-FOA-BP and SE-GCN) to supplement missing heavy-metal values and efficiently predict heavy-metal content in soil. The SA-FOA-BP model combines simulated annealing and fruit fly algorithms to optimize the parameter search method in traditional BP neural networks and improve prediction of missing heavy-metal values in soil. A spatial information fusion graph convolutional network prediction model (SE-GCN) constructs a spatial information encoder that can perceive spatial context information, and embeds it with spatial autocorrelation used for auxiliary learning to predict the heavy-metal content in soil. From the experimental results, the SE-GCN model demonstrates improved performance in terms of evaluation indicators compared with other models. Application analysis of the two improved neural network models was conducted; application scenarios and suitability were analyzed, showing that these models have practical application value for soil pollution prediction.