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Water Pollution Prevention and Control Project, 2022, 3(1); doi: 10.38007/WPPCP.2022.030102.

Support Vector Machine Algorithm in Water Quality Prediction

Author(s)

Georgy V. Moiseev

Corresponding Author:
Georgy V. Moiseev
Affiliation(s)

Institute of IT & Computer Science, Afghanistan

Abstract

Water quality assessment and prediction is an essential part of aquatic research. Its purpose is to accurately grasp the situation of water quality and pollutants and predict the development trend in the future, which is a basic task of managing, protecting and maintaining the water environment. The increasingly serious water pollution has destroyed the ecological environment, affected people’s life and health, and seriously restricted social and economic development. Based on this, it is necessary to predict the water quality. Therefore, this paper designed support vector machine (SVM) algorithm, designed water quality prediction model, and utilized it to water quality prediction. At the same time, experiments were designed to compare the SVM algorithm with the traditional neural network algorithm. The average concentration value of permanganate index and ammonia nitrogen concentration of water quality parameters were selected for prediction and analysis, and finally a feasible conclusion was reached. Compared with the traditional neural network algorithm, SVM algorithm could greatly improve the prediction effect of the average concentration of permanganate index and ammonia nitrogen concentration. The error between the predicted average concentration of permanganate index and the actual value was less than 3%, which was conducive to improving the prediction effect of water quality. The research results showed that SVM algorithm had good prediction effect in water quality prediction and could expand the application range.

Keywords

Water Quality Prediction, Support Vector Machines, Neural Networks, Water Resources

Cite This Paper

Georgy V. Moiseev. Support Vector Machine Algorithm in Water Quality Prediction. Water Pollution Prevention and Control Project (2022), Vol. 3, Issue 1: 12-21. https://doi.org/10.38007/WPPCP.2022.030102.

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