AI-driven prediction and risk assessment of heavy metal contamination in mining-affected soil and groundwater
Dehua Zeng
China Nonferrous Metals Geology Institute Co., Ltd
DOI: https://doi.org/10.59429/pest.v7i1.9473
Keywords: Mining - affected soil and groundwater; Heavy metal contamination; Machine learning; Deep learning; Diffusion trend prediction; Risk assessment
Abstract
Mining activities often lead to heavy metal contamination in soil and groundwater, posing significant threats to the environment and human health. This study focuses on leveraging machine learning and deep learning models to predict the diffusion trends of heavy metals in mining - affected areas and conduct comprehensive risk assessments. By analyzing large - scale data related to soil and groundwater quality, geological conditions, and mining activities, we aim to provide more accurate and timely information for environmental management and decision - making.
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