Open Access

ARTICLE

Correlation Analysis of Turbidity and Total Phosphorus in Water Quality Monitoring Data

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

1 College of Engineering and Design, Hunan Normal University, Changsha, 410081, China
2 Hunan Institute of Metrology and Test, Changsha, 410014, China
3 Big Data Institute, Hunan University of Finance and Economics, Changsha, 410205, China
4 Department of Information and Communication Technology, University Malaysia Sabah, Sabah, 88400, Malaysia

* Corresponding Author: Jianjun Zhang. Email:

Journal on Big Data 2023, 5, 85-97. https://doi.org/10.32604/jbd.2022.030908

Received 08 January 2023; Accepted 13 March 2023; Issue published 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 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.

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Keywords

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