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Dissolved organic matter (DOM) in surface water, which serves as a significant component within the carbon cycle, plays a vital role in the biogeochemical cycle of aquatic ecosystems. The triplet excited state of DOM (³DOM^*), singlet oxygen (¹O₂), along with hydroxyl radicals (·OH), which are the photochemical products generated from DOM, were taken as the research targets. Three regression algorithms were employed to constructed the machine learning (ML) models predictive models for evaluating importance of features including environmental factors, ultraviolet-visible light parameters and fluorescence parameters. The ML model based on the categorical boosting algorithm exhibited better performance for the prediction targets in the test set, with R² values of 0.9344, 0.8091, and 0.7775, respectively. Temperature, DOC, and pH were identified as the most important features for predicting ³DOM^*, ¹O₂ and ·OH, respectively. When applying the chained ML model incorporating with the categorical boosting algorithm, the prediction performance for ¹O₂ and ·OH was further optimized. For ¹O₂ the R² value in the test set increased from 0.8091 to 0.8233, and the RMSE decreased from 1.322 to 0.418. For ·OH the R² value in the test set rose from 0.7775 to 0.8094, and the RMSE dropped from 2.982 to 0.312. Additionally, the ML models perform better than the partial least squares regression model in predicting ³DOM^*, ¹O₂ and ·OH, indicating that the ML models possessed strong capability to handle non-linear relationships. The feature importance analysis based on ML successfully distinguished the key features in the DOM photochemical reaction. This finding can provide direct evidence for the intensive management of numerous influencing factors.

, authors=Bing LIU^{1, *}, Kai-ge SHI¹, Yan-min CHEN¹, Xin LU¹, Shuai-jun HAN¹, Li GU², authorsList=Bing LIU, Kai-ge SHI, Yan-min CHEN, Xin LU, Shuai-jun HAN, Li GU, authorCompany=null, correspAuthors=Bing LIU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314917586136031983, articleId=1314917585603355370, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于可解释机器学习的地表水DOM光化学性质预测, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

以DOM光化学产物三重激发态DOM（³DOM^*）、单线态氧（¹O₂）、羟基自由基（·OH）作为研究目标，分别使用3种回归算法构建机器学习（ML）模型，评估环境因素、紫外-可见光参数和荧光参数作为特征的重要性．结果表明，分类梯度提升算法ML模型在测试集中对预测目标的表现更好，R²分别为0.9344、0.8091和0.7775．温度、DOC和pH值分别是预测³DOM^*、¹O₂和·OH的最重要特征．应用分类梯度提升算法链式ML模型时，对¹O₂和·OH预测性能有所优化，¹O₂在测试集的R²由0.8091增加至0.8233，RMSE由1.322降至0.418；·OH在测试集的R²由0.7775上升至0.8094，RMSE由2.982降至0.312．另外，ML模型对³DOM^*、¹O₂和·OH的预测表现均优于偏最小二乘法回归模型，表明ML模型对非线性关系具有较强的处理能力．基于ML的特征重要性分析识别出DOM光化学反应中的关键特征，可为众多影响因素的集约化处理提供直接证据．

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刘冰（1978-），男，河南许昌人，博士，副教授，主要研究方向为水体中含氮消毒副产物及前体物识别和控制城市污水处理原理与技术．发表论文30余篇．liubing_982002@163.com.

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刘冰（1978-），男，河南许昌人，博士，副教授，主要研究方向为水体中含氮消毒副产物及前体物识别和控制城市污水处理原理与技术．发表论文30余篇．liubing_982002@163.com.

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