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.

Prediction of Total Petroleum Hydrocarbons and Heavy Metals in Acid Tars Using Machine Learning

“…The prediction of soil petroleum hydrocarbon concentration is achieved by machine learning and the resistivity tomography method [36]. Since the field measurement of soil heavy metal content involves significant costs, methods have been developed to estimate soil heavy metals based on remote sensing images and machine learning [37][38][39]. Also, some measurements were studied and published using machine learning predictions of soil pH [40].…”

“…The lack of data was noted while it was being obtained. I specified that, with the exception of a few works in the literature, the uses of ML algorithms are published separately for soil pH, TPH, and heavy metals [34][35][36][37][38]. We specified that with the exception of a few works in the literature, the uses of the ML algorithm are published separately for soil pH, TPH, and heavy metals.…”