首页|基于机器学习模型的北海市银海区小流域地表水中铵氮污染预测

基于机器学习模型的北海市银海区小流域地表水中铵氮污染预测

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北海市银海区小流域(冯家江流域、三合口江流域和福成河流域)地表水富营养化问题严峻,然而对地表水中铵氮(NH4+-N)浓度的预测研究较少.采用多元线性回归、支持向量机和随机森林3种机器学习模型,利用北海市银海区小流域地表水水质全分析数据,预测了研究区地表水中NH4+-N浓度的空间分布.结果表明:随机森林模型的均方根误差中值最低,拟合效果最佳,预测得到的地表水中NH4+-N浓度空间分布与实际NH4+-N浓度分布高度一致;NH4+-N浓度超过地表水劣V类限值2 mg/L的地表水主要分布在冯家江流域;PO43-、HCO3-和总碱度是研究区地表水中NH4+-N污染最显著的指示因子,这与人类活动密不可分.
Prediction of ammonium nitrogen pollution in surface water in small watersheds of Yinhai,Beihai City based on machine learning models
The issue of eutrophication in surface water of small watersheds(Fengjiajiang River,Sanhekou River,and Fucheng River)in Yinhai,Beihai City,is of grave concern.However,there has been limited re-search on the prediction of surface water ammonium nitrogen(NH4+-N)levels.In this study,three machine learning models,namely multiple linear regression,support vector machine and random forest,were em-ployed to predict the spatial distribution of NH4+-N in small watersheds of Yinhai,Beihai City,using com-prehensive water quality analysis data.The results indicate that in multiple experiments,the random forest model consistently exhibited the lowest median root mean square error and the best fitting performance,showing a high degree of consistency with observed NH4+-N distribution in surface water.Based on the re-sults,areas with NH4+-N concentrations exceeding the Class V water quality standard limit of 2 mg/Lare mainly in Fengjiajiang River.Furthermore,PO43-,HCO3-,and total alkalinity are identified as the most sig-nificant indicator factors contributing to the enrichment of NH4+-N in surface water,highlighting the unde-niable influence of human activities on surface water pollution.

surface waterammonium nitrogen(NH4-N)pollutionmachine learning modelBeihai City

涂兵、杨博、王令占、李响、谢国刚、马筱、张宗言

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中国地质调查局武汉地质调查中心(中南地质科技创新中心),湖北武汉 430205

地表水 铵氮污染 机器学习模型 北海市

中国地质调查局地质调查项目中国地质调查局地质调查项目中国地质调查局地质调查项目

DD20211385DD20211139DD20230104

2024

安全与环境工程
中国地质大学

安全与环境工程

CSTPCD北大核心
影响因子:1.03
ISSN:1671-1556
年,卷(期):2024.31(1)
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