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基于无人机多光谱影像的城市河道水质反演

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利用无人机多光谱反射率影像和同步实测水质数据,建立基于机器学习的水质参数反演模型,并将该模型应用于张家港河.结果表明,基于XGBoost和随机森林的特征变量重要性分析方法选择氨氮反演的最佳波段组合,确定用随机森林进行氨氮反演精度较高,其测试集决定系数为0.91,平均绝对百分比误差为23.57%;反演结果能从空间上精细地反映张家港河光明村段支流水质的特点,并直观展示水质超标重点区域.
Inversion of Urban River Water Quality Based on UAV Multispectral Image
A water quality parameter inversion model was established based on machine learning and by using UAV multispectral reflectance image to detect water quality.Applying this model to inverting the water quality of Zhangjiagang River,the results indicated that random forest algorithm had high accuracy in inver-ting ammonia nitrogen on the optimal band combination selected by feature variable importance analysis based on XGBoost and random forest.The determination coefficient of the test set was 0.91,and the average absolute per-centage error was 23.57%.The inversion results could accurately reflect the water quality characteristics of the tributaries in Guangmingcun section of Zhangjiagang River in space,and visually displayed the key areas where water quality exceeding the standard.

Ammonia nitrogenMultispectral imageInverse modelUnmanned aerial vehicleWater quality

何炜琪、吴志杰、王紫安

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清华苏州环境创新研究院,江苏 苏州 215163

氨氮 多光谱影像 反演模型 无人机 水质

国家重点研发计划基金资助项目南京市水务科技基金资助项目

2020YFC1807402202307

2024

环境监测管理与技术
江苏省环境监测中心,南京市环境监测中心站

环境监测管理与技术

CSTPCD北大核心
影响因子:1.086
ISSN:1006-2009
年,卷(期):2024.36(5)
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