@Article{jbd.2022.030908,
AUTHOR = {Wenwu Tan, Jianjun Zhang, Xing Liu, Jiang Wu, Yifu Sheng, Ke Xiao, Li Wang, Haijun Lin, Guang Sun, Peng Guo},
TITLE = {Correlation Analysis of Turbidity and Total Phosphorus in Water Quality Monitoring Data},
JOURNAL = {Journal on Big Data},
VOLUME = {5},
YEAR = {2023},
NUMBER = {1},
PAGES = {85--97},
URL = {http://www.techscience.com/jbd/v5n1/54965},
ISSN = {2579-0056},
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 total phosphorus and turbidity outliers were calculated after cleaning, and the correlation between them was analyzed. Secondly, the correlation coefficients of different times and frequencies were used to predict the values for the next five days, and the data trend was predicted by python visualization. Finally, the real value was compared with the predicted value data, and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.},
DOI = {10.32604/jbd.2022.030908}
}