A novel Hybrid Wavelet-Locally Weighted Linear Regression (W-LWLR) Model for Electrical Conductivity (EC) Prediction in Surface Water.

Rivers are the most common and vital sources of water, which play a fundamental role in ecological systems and human life. Water quality assessment is a major element of managing water resources and accurate prediction of water quality is very essential for better management of rivers. The electrical conductivity (EC) is known as one of the most important water quality parameters to predict salinity and mineralization of water. The present study introduces a novel hybrid wavelet-locally weighted linear regression (W-LWLR) method to predict the monthly EC of the Sefidrud River in Iran. 240 monthly discharge (Q) and EC samples, over a period of 20 years, were collected. The data were divided into two frequency components at two decomposition levels using the mother wavelet Bior 6.8. To compare the performance of various methods, the standalone LWLR, support vector regression (SVR), wavelet support vector regression (W-SVR), autoregressive integrated moving average (ARIMA), wavelet ARIMA (W-ARIMA), multivariate linear regression (MLR), and wavelet MLR (W-MLR) were also used. The discrete wavelet transform (DWT) was coupled with the LWLR, SVR, and ARIMA to create the W-LWLR, W-SVR, W-ARIMA methods to predict the EC parameter. The comparisons demonstrated that the W-LWLR was more accurate and efficient than the LWLR, SVR, W-SVR, ARIMA, and W-ARIMA methods. The correlation coefficient (R) values were 0.973, 0.95, 0.565, 0.473, 0.425, 0.917 for the W-LWLR, W-SVR, LWLR, SVR, ARIMA, and W-ARIMA methods, respectively. Further, the root mean square error (RMSE) of W-LWLR was 89.78, while the corresponding values for W-SVR, LWLR, SVR, ARIMA, W-ARIMA, MLR, and W-MLR were 123.50, 319.95, 341.20, 350.153, 155.292, 351.774, and 157.856 respectively. The overall comparison metrics and error analysis demonstrated the superiority of the new proposed W-LWLR method for water quality prediction.