机器学习在土壤污染识别中的应用研究进展

Research Progress in the Application of Machine Learning in Soil Contamination Identification

随着大数据时代的到来,机器学习在环境保护领域中得到了广泛应用。机器学习方法可从大量复杂数据中提取有效信息,在处理土壤污染识别问题上表现出明显优势。为了解机器学习在土壤污染识别中的研究进展,对Web of Science数据库中2007—2022年间相关领域文献进行文献计量分析,结果显示,相关领域研究论文发表量呈逐年上升趋势。此外,通过关键词共现分析和联系强度分析发现,基于机器学习方法构建预测模型已成为农业土壤污染识别、土壤重金属污染来源及其健康风险评估等方面的研究热点。已有研究中,常用的机器学习方法包括随机森林、支持向量机、人工神经网络和深度学习等。然而当前机器学习在土壤污染识别应用中仍存在一定的局限性,未来需要加强数据质量的提升和预测模型的优化,以进一步提升机器学习在该领域的应用效果。

With the rapid emergence of the big data era,the utilization of machine learning techniques has become widespread in the field of environmental protection.These methods enable the extraction of valuable insights from vast and intricate datasets,offering distinct advantages in addressing soil pollution identification challenges.To comprehensively understand the research progress of machine learning in this domain,a bibliometric analysis was conducted on literature from related fields in the Web of Science database spanning the years from 2007 to 2022.The findings of this analysis indicate a steady increase in the number of research papers published in the relevant fields.Furthermore,the keyword co-occurrence and link strength analyses reveal a significant research focus on constructing predictive models for identifying agricultural soil pollution,soil heavy metal pollution,and health risk assessment using machine learning methods.Common machine learning techniques employed in these studies include random forest,support vector machine,artificial neural network,and deep learning.In addition to highlighting the positive advancements,it is essential to acknowledge the limitations of machine learning in soil pollution identification applications.Further improvement in data quality and optimization of prediction models are crucial areas that should be addressed to enhance the effectiveness of machine learning approaches in this field in the future.