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Modeling Daily and Monthly Water Quality Indicators in a Canal Using a Hybrid Wavelet-Based Support Vector Regression Structure

Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
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Water 2020, 12(5), 1476; https://doi.org/10.3390/w12051476
Received: 24 April 2020 / Revised: 30 April 2020 / Accepted: 19 May 2020 / Published: 21 May 2020
(This article belongs to the Special Issue Water-Quality Modeling)
Accurate prediction of water quality indicators plays an important role in the effective management of water resources. The models which studied limited water quality indicators in natural rivers may give inadequate guidance for managing a canal being used for water diversion. In this study, a hybrid structure (WA-PSO-SVR) based on wavelet analysis (WA) coupled with support vector regression (SVR) and particle swarm optimization (PSO) algorithms was developed to model three water quality indicators, chemical oxygen demand determined by KMnO4 (CODMn), ammonia nitrogen (NH3-N), and dissolved oxygen (DO), in water from the Grand Canal from Beijing to Hangzhou. Modeling was independently conducted over daily and monthly time scales. The results demonstrated that the hybrid WA-PSO-SVR model was able to effectively predict non-linear stationary and non-stationary time series and outperformed two other models (PSO-SVR and a standalone SVR), especially for extreme values prediction. Daily predictions were more accurate than monthly predictions, indicating that the hybrid model was more suitable for short-term predictions in this case. It also demonstrated that using the autocorrelation and partial autocorrelation of time series enabled the construction of appropriate models for water quality prediction. The results contribute to water quality monitoring and better management for water diversion. View Full-Text
Keywords: water quality modeling; time series prediction; wavelet analysis (WA); support vector regression (SVR); particle swarm optimization (PSO) algorithms water quality modeling; time series prediction; wavelet analysis (WA); support vector regression (SVR); particle swarm optimization (PSO) algorithms
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Wang, Y.; Yuan, Y.; Pan, Y.; Fan, Z. Modeling Daily and Monthly Water Quality Indicators in a Canal Using a Hybrid Wavelet-Based Support Vector Regression Structure. Water 2020, 12, 1476.

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