Remote Sensing Retrieval of Urban Lake DOC Concentration Based on Optimized Random Forest Algorithm

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Remote Sensing Retrieval of Urban Lake DOC Concentration Based on Optimized Random Forest Algorithm

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[1]LI Aimin,WANG Hailong,XU Youcheng.Remote Sensing Retrieval of Urban Lake DOC Concentration Based on Optimized Random Forest Algorithm[J].Journal of Zhengzhou University (Engineering Science),2022,43(06):90-96.[doi:10.13705/j.issn.1671-6833.2022.06.007]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 06 Page number: 90-96 Column: Public date: 2022-09-02

Title:: Remote Sensing Retrieval of Urban Lake DOC Concentration Based on Optimized Random Forest Algorithm

Author(s):: LI Aimin ¹; WANG Hailong²; XU Youcheng ²; 1.School of Geo-science and Technology, Zhengzhou University, Zhengzhou 450001, China;
2.School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China

Keywords:: dissolved organic carbon; remote sensing inversion; Planet; random forest; Bayesian optimization

CLC:: P237

DOI:: 10.13705/j.issn.1671-6833.2022.06.007

Abstract:: In remote sensing monitoring of dissolved organic carbon (DOC) content in urban lakes, the traditional regression model is difficult to describe the nonlinear relationship and can not meet the accuracy requirements. In this study, Bayesian optimization algorithm was introduced into the parameter optimization of random forest model, and a DOC concentration inversion method of urban lakes based on Bayesian optimization random forest model (BO-RF) was proposed. Taking the water area of Tiande Lake in Zhengzhou city as an example, the remote sensing inversion method of DOC concentration in urban lakes was studied based on high spatialtemporal resolution Planet satellite image data and measured DOC water quality data. Through PEARSON correlation analysis, the results showed that the best band combination of Planet satellite image band for retrieving DOC concentration was B2/ B4. The determination coefficient R2 of the band ratio model obtained by the traditional regression method was 0. 466, and the root mean square error RMSE was 0. 515 mg/ L, which could not meet the accuracy requirements. The modeling accuracy was improved by using support vector machine and BP neural network, the fitting R2 was 0. 772 and 0. 806, respectively, and the root mean square error RMSE was 0. 328 mg/ L and 0. 302 mg/ L, respectively. Bayesian optimization algorithm was introduced to optimize the random forest model to obtain the BO-RF model, and its fitting degree R2 was 0. 865 and root mean square error RMSE was 0. 253 mg/ L. The BO-RF fit of the optimized model was good, and the accuracy of the model was significantly improved. The Bayesian optimized random forest BO-RF algorithm was more suitable for retrieving DOC concentration in Tiande lake, which could provide a reference for remote sensing monitoring of urban lake water quality.

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Last Update: 2022-10-03