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Monitoring stations have been established to combat water pollution, improve the ecosystem, promote human health, and facilitate drinking water production. However, continuous and extensive monitoring of water is costly and time-consuming, resulting in limited datasets and hindering water management research. This study focuses on developing an optimized K-nearest neighbor (KNN) model using the improved grey wolf optimization (I-GWO) algorithm to predict dry residue quantities. The model incorporates 20 physical and chemical parameters derived from a dataset of 400 samples. Cross-validation is employed to assess model performance, optimize parameters, and mitigate the risk of overfitting. Four folds are created, and each fold is optimized using 11 distance metrics and their corresponding weighting functions to determine the best model configuration. Among the evaluated models, the Jaccard distance metric with inverse squared weighting function consistently demonstrates the best performance in terms of statistical errors and coefficients for each fold. By averaging predictions from the models in the four folds, an estimation of the overall model performance is obtained. The resulting model exhibits high efficiency, with remarkably low errors reflected in the values of R, R2, R2ADJ, RMSE, and EPM, which are reported as 0.9979, 0.9958, 0.9956, 41.2639, and 3.1061, respectively. This study reveals a compelling non-linear correlation between physico-chemical water attributes and the content of dry tailings, indicating the ability to accurately predict dry tailing quantities. By employing the proposed methodology to enhance water quality models, it becomes possible to overcome limitations in water quality management and significantly improve the precision of predictions regarding critical water parameters.

A B S T R A C TWater desalination is one of the most important factors that can help in developing remote areas and the desert. A critical technical parameter of desalination applications is the way the system is powered. This decision is taken according to the selected method of desalination and the characteristics of the candidate area. Nowadays, the method of reverse osmosis dominates globally; it requires only electricity, has a quite low specific energy demand, and can cooperate with technologies of renewable energy sources such as wind turbine and photovoltaics. Hence, renewable energy-powered reverse osmosis systems are promising technologies for brackish and seawater desalination in remote regions as they exhibit low energy consumption and can be designed according to water demand and energy resource. This study analyzes the feasibility of using wind energy to power brackish water reverse osmosis desalination units proposed for the development of the southern region of the case study country of Algeria. A reverse osmosis desalination scheme powered by a stand-alone wind turbine of 1 MW rated power is presented to elucidate its feasibility. The modeling results show that at average wind speeds, the amount of product water is sufficient to meet freshwater demand in this region. The effect of different operating and design conditions on the purified water production rate was investigated. The paper is concluded with the economic feasibility of wind-desalination systems at the selected sites.

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