从2000年到2023年,利用机器学习进行地下水污染研究的最新进展:一项图书识别分析
Xuan Li1, Guohua Liang1, Bin He1
1School of Hydraulic Engineering, Dalian University of Technology, Dalian, 116024, China.
Environmental research
|December 22, 2024
概括
机器学习有效地解决了地下水污染问题,自2020年以来,研究迅速扩大. 关键方法包括主要成分分析 (PCA) 和人工神经网络 (ANN),用于污染物预测和风险评估.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 地质地质地质地质地质地
背景情况:
- 地下水污染是影响健康和生态系统的全球关键问题.
- 机器学习 (ML) 为分析复杂的地下水数据提供了先进的功能.
- 图书统计分析提供了对研究趋势和贡献的系统概述.
研究的目的:
- 对2000-2023年地下水污染研究中的机器学习应用进行文献分析.
- 确定关键趋势,主要贡献者和普遍的方法.
- 建议未来的研究方向,以加强污染控制.
主要方法:
- 对1462篇文章进行了图书统计分析.
- 关键词频率分析以确定常见的机器学习技术.
- 出版成果的趋势分析和研究重点的演变.
主要成果:
- 自2020年以来,研究的显著增长,以中国,美国,印度和伊朗为首.
- 主要组件分析 (PCA),人工神经网络 (ANN) 和层次聚类分析 (HCA) 是主要的方法.
- 焦点从基本评估演变为污染物预测,风险评估和来源识别.
结论:
- 机器学习是理解和管理地下水污染的重要工具.
- 解决数据质量,稀缺性和模型解释性对于未来的进步至关重要.
- 优先考虑数据共享和理论引导的ML将推动有效的补救策略.
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