Yaping Luo¹, Na Guo^1,*, Dong Liu², Shuming Peng³, Xinchen Wang⁴, Jie Wu³

Revue Internationale de Géomatique, Vol.32, pp. 39-52, 2023, DOI:10.32604/RIG.2023.046014 - 20 December 2023

Abstract Using hyperspectral data collected in January and June 2022 from the Sha River, the concentrations of total nitrogen (TN) and total phosphorus (TP) were estimated using the differential method. The results indicate that the optimal bands for estimation vary monthly due to temperature fluctuations. In the TN model, the power function model at 586 nm in January exhibited the strongest fit, yielding a fit coefficient (R²) of 0.95 and F-value of 164.57 at a significance level (p) of less than 0.01. Conversely, the exponential model at 477 nm in June provided the best fit, with R² = More > Graphic Abstract

Wenwu Tan¹, Jianjun Zhang^1,*, Xing Liu¹, Jiang Wu¹, Yifu Sheng¹, Ke Xiao², Li Wang², Haijun Lin¹, Guang Sun³, Peng Guo⁴

Journal on Big Data, Vol.5, pp. 85-97, 2023, DOI:10.32604/jbd.2022.030908 - 26 December 2023

Abstract At present, water pollution has become an important factor affecting and restricting national and regional economic development. Total phosphorus is one of the main sources of water pollution and eutrophication, so the prediction of total phosphorus in water quality has good research significance. This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform. By constructing the attribute object mapping relationship, the correlation between the two indicators was analyzed and used to predict the future data. Firstly, the monthly mean and daily mean concentrations of More >