机器学习在河水质量管理中的应用:一篇综述
Sanja Cojbasic1, Sonja Dmitrasinovic2, Marija Kostic2
1Faculty of Technical Sciences, Department of Environmental Engineering and Occupational Safety and Health, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
概括
机器学习 (ML) 为环境工程提供了强大的数据驱动解决方案,特别是用于预测河流水质. 本研究回顾了常见的ML算法,如人工神经网络和用于此目的的支持矢量机器.
科学领域:
- 环境工程 环境工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 在环境工程中越来越重要,用于分析复杂的非线性环境问题.
- 河流水质管理显著受益于ML的数据驱动方法,以了解各种参数之间的复杂关系.
研究的目的:
- 提供用于监测和预测河流水质量的ML算法的全面概述.
- 突出常用的监督学习算法,并讨论混合模型的潜力.
主要方法:
- 审查监督学习算法,包括人工神经网络 (ANN),支持矢量机器 (SVM),随机森林 (RF),决策树 (DT) 和深度学习 (DL).
- 探索ML模型监测和预测的各种水质参数,例如溶解氧 (DO),生化氧需求 (BOD),化学氧需求 (COD),度,离子度,重金属,pH值和温度.
- 考虑数据质量,参数选择和输入数据组合作为影响预测准确性的关键因素.
主要成果:
- 监督学习算法是当前河水质量预测研究中最常见的.
- 输入数据的选择和质量,以及所选择的算法,对于精确的基于ML的水质预测至关重要.
- 混合ML算法显示了增强预测能力的前景.
结论:
- ML为环境工程提供了有效的工具,特别是在水质管理方面.
- 算法选择和数据预处理是ML应用在河水质量的成功的关键决定因素.
- 对混合模型的进一步研究可以提高预测准确性和可靠性.
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