关键词: 水质评价, 水质指标, 机器学习, 深度学习, 光谱模型, 指数评价法

Abstract: In water quality monitoring, the characteristic analysis and evaluation of water quality are of great significance. To overcome the limitations of the water quality index evaluation method, which are difficult to accurately and comprehensively reflect the comprehensive status of water quality, machine learning has been introduced in recent years and has been more and more widely used in the indicator prediction and evaluation of water quality. Machine learning provides a more scientific and efficient means for water quality evaluation by integrating multi-source and multi-index water data and mining the complex linear or nonlinear relationship between water quality factors and water quality. This article first summarized- 引用格式:张文恺,杨术明,马永龙,等.基于EDEM的牧场推料机器人参数优化设计与试验［J］.河北大学学报(自然科学版),2026,46(3):225-236.引用格式:李艳坤,胡春阳,蒋晨帅,等.水质评价中机器学习与指数评价法的研究与应用［J］.河北大学学报(自然科学版),2026,46(3):288-298.DOI:10.3969/j.issn.1000-1565.2026.03.007水质评价中机器学习与指数评价法的研究与应用李艳坤¹,胡春阳¹,蒋晨帅²,曾祎程¹,梁旭阳¹(1.华北电力大学 环境科学与工程系,河北 保定 071003;2.白洋淀流域生态环境监测中心,河北 保定 071500)摘 要:在水质监测中,水质特征分析与评价具有重要意义.为克服水质指数评价法难以精确、全面反映水质综合状况等局限性,近年来机器学习被引入,并广泛地应用于水质指标的预测和评价中.机器学习通过融合多源多指标水体数据、挖掘水质各因子与水质之间复杂的线性或非线性关系,为水质评价提供了更科学、高效的手段.本文首先总结了单因子指数法等水质指数评价法的原理、特点及其应用现状.综述了随机森林、神经网络等机器学习算法及其结合光谱构建的分析模型在水质评价中的具体应用,深入分析了各类方法用于水质评价的原理、特点与适用场景;介绍了水质指数评价法和机器学习算法联用在水质检测与评价中的应用;提出应用机器学习技术时所存在的问题及相应解决措施.旨在为基于机器学习、光谱分析模型的水质评价提供理论参考和实践借鉴,藉以推动水质检测、评价与预测的研究和应用发展.关键词:水质评价;水质指标;机器学习;深度学习;光谱模型;指数评价法中图分类号:X832 文献标志码:A 文章编号:1000-1565(2026)03-0288-11DOI:10.3969/j.issn.1000-1565.2026.03.007Research and application of machine learning and index evaluation method in water quality evaluationLI Yankun¹, HU Chunyang¹, JIANG Chenshuai², ZENG Yicheng¹, LIANG Xuyang¹(1. Department of Environmental Science and Engineering, North China Electric Power University,Baoding 071003, China; 2. Baiyangdian Basin Ecological Environment Monitoring Center, Baoding 071500, China)Abstract: In water quality monitoring, the characteristic analysis and evaluation of water quality are of great significance. To overcome the limitations of the water quality index evaluation method, which are difficult to accurately and comprehensively reflect the comprehensive status of water quality, machine learning has been introduced in recent years and has been more and more widely used in the indicator prediction and evaluation of water quality. Machine learning provides a more scientific and efficient means for water quality evaluation by integrating multi-source and multi-index water data and mining the complex linear or nonlinear relationship between water quality factors and water quality. This article first summarized- 收稿日期:2025-05-21;修回日期:2025-09-04 基金项目:国家大学生创新创业训练计划项目(X2024103) 第一作者:李艳坤(1977—),女,华北电力大学副教授,主要从事化学计量学、环境分析化学方向研究.E-mail:liyankun_ncepu@foxmail.com 第3期李艳坤等:水质评价中机器学习与指数评价法的研究与应用河北大学学报(自然科学版) 第46卷the principles, characteristics and current application status of water quality index evaluation methods such as Single Factor Index method. It reviewed the specific applications of machine learning algorithms such as Random Forest and Neural Network, and specific applications of analysis model constructed by machine learning algorithms and spectra data in water quality evaluation. It deeply analyzed the principles, characteristics and application scenarios of various methods for water quality evaluation. It also introduced the joint application of water quality index evaluation methods and machine learning algorithms in water quality detection and evaluation. The problems and solutions in the application of machine learning technology were also proposed. The aim is to provide theoretical references and practical references for water quality evaluation based on machine learning and spectral analysis model to promote the research and application development of water quality detection, evaluation and prediction.

Key words: water quality evaluation, water quality index, machine learning, deep learning, spectral model, index evaluation method