Prediction of Zinc, Cadmium, and Arsenic in European Soils Using Multi-End Machine Learning Models

Abstract

Heavy metal contamination in soil is a major environmental and public health concern, especially in regions with substantial industrial and agricultural activities. Conventional predictive models often focus on single contaminants, limiting their utility for comprehensive environmental monitoring. This study addressed these limitations by developing an advanced multi-end ensemble convolutional neural network model capable of simultaneously predicting the concentrations of cadmium, arsenic, and zinc in European soils. A comprehensive dataset with 18 diverse factors was prepared, including soil properties, climatic factors, and anthropogenic activities. Moreover, the model compared four ensemble learning techniques in contamination prediction, including simple averaging, snapshot ensembles, integrated stacking, and separate stacking. Among these, the separate stacking model with random forest regressor meta-model achieved the highest accuracy, with a mean spared error of 0.0378, a mean absolute error of 0.0785, and a coefficient of determination of 0.79 in the testing phases. Sensitivity analysis highlighted farming area, road length, nitrogen content, and mean annual temperature as key factors influencing metal concentrations. To enhance accessibility, a GUI-based web application was developed, allowing users to enter relevant factors and receive real-time predictions of contamination levels. This application empowers stakeholders, such as environmental regulators and policymakers, to make informed, data-driven decisions for targeted remediation. These findings underscore the critical role of integrated machine learning approaches in environmental science, offering a powerful tool for identifying contamination hotspots, supporting soil health management, and promoting sustainable land use.

Environmental Implication

The study reveals significant environmental implications, showing that soil contamination by arsenic, cadmium, and zinc across Europe is heavily influenced by human activities, particularly intensive agricultural practices that enhance the mobility of these heavy metals. Findings indicate that factors such as farming area, road length, nitrogen content, and mean annual temperature play a crucial role in metal accumulation. The advanced, multi-ended predictive models developed in this research, paired with an accessible web-based application, provide a practical tool for policymakers, researchers, and land managers. This tool enables real-time contamination assessments and supports the creation of policies aimed at controlling soil pollution, safeguarding public health, and promoting sustainable agricultural practices. These findings underscore the urgent need for integrated soil management strategies and contamination mitigation efforts, emphasizing how targeted actions can reduce the long-term impacts of heavy metal pollution on ecosystems and human health.