Abstract
Suspended solids concentration (SSC) is an important indicator of the degree of water pollution. However, when using an empirical or semi-empirical model adapted to some of the inland waters to estimate SSC on unmanned aerial vehicle (UAV)-borne hyperspectral images, the accuracy is often not sufficient. Thus, in this study, we attempted to use the particle swarm optimization (PSO) algorithm to find the optimal parameters of the least-squares support vector machine (LSSVM) model for the quantitative inversion of SSC. A reservoir and a polluted riverway were selected as the study areas. The spectral data of the 36-point and 29-point 400–900 nm wavelength range on the UAV-borne images were extracted. Compared with the semi-empirical model, the random forest (RF) algorithm and the competitive adaptive reweighted sampling (CARS) algorithm combined with partial least squares (PLS), the accuracy of the PSO-LSSVM algorithm in predicting the SSC was significantly improved. The training samples had a coefficient of determination ( R 2 ) of 0.98, a root mean square error (RMSE) of 0.68 mg/L, and a mean absolute percentage error (MAPE) of 12.66% at the reservoir. For the polluted riverway, PSO-LSSVM also performed well. Finally, the established SSC inversion model was applied to UAV-borne hyperspectral remote sensing (HRS) images. The results confirmed that the distribution of the predicted SSC was consistent with the observed results in the field, which proves that PSO-LSSVM is a feasible approach for the SSC inversion of UAV-borne HRS images.
Highlights
Certain water quality parameters (WQPs) in a water body can cause changes in the optical properties of the water surface
This paper aims to provide a feasible reference for the estimation of solids concentration (SSC) in inland waters by unmanned aerial vehicle (UAV)-borne hyperspectral remote sensing (HRS) images combined with a machine learning algorithm
The support vector machine (SVM) kernel is embedded in many machine learning toolkits, including LIBSVM, MATLAB, SAS, SVMlight, Scikit-Learn, OpenCV, etc., so it is easy to carry out the related algorithms and theoretical research
Summary
Certain water quality parameters (WQPs) in a water body can cause changes in the optical properties of the water surface. The semi-empirical approach describes the functional relationship between remote sensing data and the WQPs through statistical regression, and it is a common way to achieve a high inversion accuracy in certain problems. Singh et al [15] established a support vector machine (SVM) classification and regression model, which was used for surface water quality monitoring These inversion methods still lack a certain physical meaning, the automatic prediction ability based on a complex model can often guarantee the accuracy of the remote sensing inversion of WQPs, so it is still an important research direction. In this study, based on the development prospects of UAV-borne HRS images for water environment monitoring and the ability of machine learning to automate prediction in regression modeling, a M600 Pro UAV manufactured by DJI Lnc. This paper aims to provide a feasible reference for the estimation of SSC in inland waters by UAV-borne HRS images combined with a machine learning algorithm
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