审查基于机器学习的替代模型的地下水污染物建模模型
Jiannan Luo1, Xi Ma1, Yefei Ji2
1Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchun 130021, China.
Environmental research
|September 30, 2023
概括
机器学习替代模型显著提高了地下水污染物建模效率. 本综述分析了120项研究,突出了人工神经网络等关键应用和方法,以更好地管理地下水.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 计算科学 计算科学
背景情况:
- 地下水污染物的数值模型在污染源识别和不确定性分析等应用中面临着计算挑战.
- 基于机器学习 (ML) 的替代模型提供了一个有效的替代方案来加速这些复杂的地下水模拟.
研究的目的:
- 审查和综合基于ML的替代模型中的最新技术,用于地下水污染物建模.
- 确定这个快速发展的领域的关键应用,挑战和未来的研究方向.
主要方法:
- 1994年至2022年间发表的120篇关于ML替代模型在地下水污染物建模中的综合文献综述.
- 分析常见的采样方法 (例如,拉丁式超立方体采样) 和替代模型构建技术 (例如,人工神经网络,Kriging).
主要成果:
- 六个主要应用主导了该领域:污染源识别,修复设计,沿海含水层管理,不确定性分析,监测网络设计和参数反转.
- 人工神经网络和Kriging是普遍存在的替代建模技术,拉丁语超立方体采样是常见的数据生成方法.
结论:
- 机器学习替代模型对于克服地下水建模中的计算局限性至关重要.
- 未来的研究应该专注于解决维度的诅咒,增强模型的可转移性,并使实时应用成为可能.
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