首页|无人机高光谱遥感和集成深度置信神经网络算法用于密云水库水质参数反演

无人机高光谱遥感和集成深度置信神经网络算法用于密云水库水质参数反演

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随工业化及社会经济迅速发展,水源地面临的水体污染、恶化不断加剧,开展有效的水质监测是水源保护的重要前提.密云水库是北京重要的地表水源地,在保护首都水源安全方面发挥着重要作用.为更精准的监测密云水库水质参数及污染程度,采用4期无人机高光谱遥感数据,基于随机子空间的集成深度置信神经网络算法(EDBN),构建密云水库水质参数反演模型,对密云水库总氮(TN)、总磷(TP)两项水质参数进行反演.首先,采用基于递归特征消除法对高光谱影像降维处理,将光谱数据与地面水质监测数据进行叠加,通过最小化训练过程中误差来确定隐藏层数、神经节点数等网络结构参数;然后采用由知识源域向网络迁移方式逐步拓展网络,对密云水库总氮和总磷两个水质参数进行训练并对结果进行验证;最后,反演密云水库潮河大坝和白河大坝区域水质参数,揭示其主要水质参数时空演变规律.研究结果显示:①构建总氮、总磷反演模型R2分别为0.835 5、0.770 3,MSE分别为0.015 3、0.000 8,这表明基于随机子空间的集成深度置信神经网络算法模型对密云水库水质参数反演效果较好;②密云水库总氮浓度变化随季节发生一定波动,在夏季浓度较低,秋季相对较高.总磷浓度变化相对平稳,表明密云水库周边区域对磷污染控制效果良好;③白河大坝区域水质优于潮河大坝区域,总氮浓度相对偏高,整体处于Ⅲ类水平.而总磷浓度较低,整体处于Ⅱ类水平,较好时可以达到Ⅰ类水平.整体水质可以满足饮用水源的标准,但仍需加强对氮、磷污染物有效监管.研究结果将为密云水库水质高效监测与水源保护提供重要科学依据.
Retrieval Model for Water Quality Parameters of Miyun Reservoir Based on UAV Hyperspectral Remote Sensing Data and Deep Neural Network Algorithm
With the rapid development of industrialization and social economy,water pollution and deterioration of water sources are increasingly aggravated,and effective water quality monitoring is an important prerequisite for water source protection.Miyun Reservoir is an important surface water source in Beijing,which plays an important role in protecting water safety in the capital.In order to monitor the water quality parameters and pollution degree of Miyun Reservoir more accurately,this study used four phases of UAV hyperspectral remote sensing data to construct a water quality parameter retrieval model based on a deep neural network algorithm.Total nitrogen(TN)and total phosphorus(TP)water quality parameters in Miyun Reservoir were retrieved.Firstly,the hyperspectral image dimensionality reduction processing based on the recursive feature elimination method was used,and the spectral data and groundwater quality monitoring data were superimposed.The network structure parameters,such as the number of hidden layers and the number of ganglion points,were determined by minimizing the error in the training process.Then,the migration method gradually expanded the network from knowledge source domain to network,and the water quality parameters of TN and TP concentration in Miyun reservoir were trained and verified.Finally,the water quality parameters of Chaohe Dam and Baihe Dam in Miyun Reservoir were retrieved to reveal the spatio-temporal evolution of the main water quality parameters.The results show that ① the R2 of the TN and TP concentration retrieval models constructed in this study are 0.835 5 and 0.770 3,and the MSE is 0.015 3 and 0.000 8.The Ensemble Deep Belief Network(EDBN)model based on random subspace has a better retrieval effect on water quality parameters.(2)TN concentration in Miyun Reservoir fluctuates with seasons,with a low concentration in summer and a relatively high concentration in autumn.The change in TP concentration is relatively stable,indicating that the control effect of phosphorus pollution in the surrounding area of Miyun Reservoir is good.③The water quality of the Baihe Dam was better than that of the Chaohe Dam.The seasons obviously affected the changes of the former,while the latter was significantly affected by human activities.The TN concentration of Miyun reservoir was in Class Ⅲ,and the TP was generally in Class Ⅱ.The water quality can meet the standards of drinking water sources,but it is still necessary to strengthen the supervision of nitrogen and phosphorus pollution.These results will provide an important scientific basis for efficiently monitoring water quality and water resources protection in the Miyun reservoir.

UAV hyperspectralDeep neural network algorithmWater quality retrievalMiyun Reservoir

乔智、姜群鸥、律可心、高峰

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北京林业大学水土保持学院,北京 100038

北京林业大学水土保持与荒漠化防治教育部重点实验室,北京 100038

无人机高光谱 深度神经网络算法 水质反演 密云水库

国家科技重大专项项目国家科技重大专项项目国家自然科学基金项目国家自然科学基金项目国家重点研发中美合作项目

2017ZX071010042017ZX0710800241901234519090522019YFE0116500

2024

光谱学与光谱分析
中国光学学会

光谱学与光谱分析

CSTPCD北大核心
影响因子:0.897
ISSN:1000-0593
年,卷(期):2024.44(7)