Prediction and sensitivity analysis of chlorophyll a based on a support vector machine regression algorithm

• Published in: Environmental monitoring and assessment Vol. 195; no. 6; p. 698
• Main Authors: Xu, Li, Hao, Guizhen, Li, Simin, Song, Fengzhi, Zhao, Yong, Guo, Peiran
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2023; Springer Nature B.V
• Subjects: Algae; Algorithms; Analysis; Atmospheric Protection/Air Quality Control/Air Pollution; China; Chlorophyll; Chlorophyll - analysis; Chlorophyll A; Coefficients; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environment pollution; Environmental factors; Environmental Management; Environmental Monitoring; Environmental science; Eutrophication; Kernel functions; Lakes - chemistry; Limiting factors; Mathematical models; Modelling; Monitoring/Environmental Analysis; Nitrogen; Nitrogen - analysis; Outbreaks; Parameters; Phosphorus - analysis; Phytoplankton; Prediction models; Radial basis function; Rivers; Rivers - chemistry; Sensitivity analysis; Spatial variations; Support Vector Machine; Support vector machines; Water flow; Water pollution; Water quality; China; Water environmental factors; Sensitivity analysis; Eutrophication; Chlorophyll a
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-023-11276-9

Outbreaks of planktonic algae seriously affect the water quality of rivers and are difficult to control. Based on the analysis of the temporal and spatial variation characteristics of environmental factors, this study uses a support vector machine regression (SVR) algorithm to establish a chlorophyll a (Chl-a) prediction model and conduct Chl-a sensitivity analysis. In 2018, the average Chl-a content was 126.25 ug/L. The maximum total nitrogen (TN) content was 16.68 mg/L and high year-round. The average NH 4 + -N and total phosphorous (TP) contents were only 0.78 and 0.18 mg/L. The content of NH 4 + -N was higher in spring and increased significantly along the water flow, while TP decreased slightly along the water flow. We used a radial basis function kernel SVR model and tenfold cross-validation method to optimize parameters. The penalty parameter c was 1.4142, the kernel function parameter g was 1, and the training and verification errors were only 0.032 and 0.067, respectively, indicating a good model fit. Based on a sensitivity analysis of the SVR prediction model, the maximum sensitivity coefficients of Chl-a to TP and WT were 0.571 and 0.394, respectively, and the contributions were 33% and 22%, respectively. The next highest sensitivity coefficients were those of DO (0.28, 16%) and pH (0.243, 14%). The sensitivity coefficients of TN and NH 4 + -N were the lowest. According to the current water environment pollution conditions, TP is the limiting factor of Chl-a in the Qingshui River, and it is also the main prevention and control factor of phytoplankton outbreak. Graphical Abstract