Article Details

Title · Authors · Abstract · Keywords · DOI · References

Research on Pipeline Water Quality Assessment Based on Integrated Algorithm

NIE Lijun

(School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

Section：Artificial Intelligence · Vol. 2025, Issue 10 · Pages：45-49 · 2096-4706（2025）10-0045-05 · CLC number：TP399 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2025.10.009

Abstract

In this paper, the water quality comprehensive index model is used for scenario innovation. Combined with the characteristics of stable physical environment of water quality in the pipeline, the pipeline water quality model PWQI is developed based on the integrated learning algorithm. The model selects 17 pipeline water quality parameters and divides them into 5 categories. In order to avoid the single weighting of water quality parameters, the combination weighting of water quality parameters is carried out by expert opinion and entropy weighting method. The CatBoost algorithm is used for regression prediction. Compared with the traditional water quality assessment method, the results show that the PWQI model has the most notable prediction effect on the pipeline water quality, which can accurately reflect the water quality of the pipeline and provide a basis for pipeline water quality assessment.

Keywords

water quality index; integrated algorithm; combined weighting; water quality assessment

References

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About the authors

Nie Lijun (2000-), male, Han ethnicity, from Nanyang, Henan Province, is currently pursuing a master's degree in machine learning and water quality analysis.