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.

河流是最常见和最重要的水源，在生态系统和人类生活中发挥着根本作用。水质评估是管理水资源的主要要素，准确预测水质对于更好地管理河流至关重要。电导率（EC）是预测水盐度和矿化度的最重要的水质参数之一。本研究介绍了一种新颖的混合小波局部加权线性回归（W-LWLR）方法，以预测伊朗塞菲德鲁德河的月度EC。在20年的时间里，收集了240个月排放量（Q）和EC样品。使用主小波Bior 6.8将数据分为两个分解级别的两个频率分量。为了比较各种方法的性能，独立的LWLR，支持向量回归（SVR），小波支持向量回归（W-SVR），自回归积分移动平均（ARIMA），小波ARIMA（W-ARIMA），多元线性回归（MLR）和小波MLR（W-MLR）也用过的。离散小波变换（DWT）与LWLR，SVR和ARIMA耦合以创建W-LWLR，W-SVR，W-ARIMA方法来预测EC参数。比较表明，W-LWLR比LWLR，SVR，W-SVR，ARIMA和W-ARIMA方法更准确和有效。对于W-LWLR，W-SVR，LWLR，SVR，ARIMA和W-ARIMA方法，相关系数（R）值分别为0.973、0.95、0.565、0.473、0.425、0.917。此外，W-LWLR的均方根误差（RMSE）为89.78，而W-SVR，LWLR，SVR，ARIMA，W-ARIMA，MLR和W-MLR的相应值为123.50、319.95、341.20、350.153 ，分别为155.292、351.774和157.856。总体比较指标和误差分析证明了新提出的W-LWLR方法在水质预测中的优越性。