In the past few decades, global industrial development and population growth have led to a scarcity of water resources, making sustainable management of groundwater a global challenge. The Water Quality Index (WQI) serves as a comprehensive method for assessing water quality and can provide valuable recommendations at the water quality level, optimizing policies for groundwater management. However, the subjectivity and uncertainty of the traditional WQI have negative impacts on evaluation outcomes, particularly in determining indicator weights and selecting aggregation functions. The proposed water quality index for groundwater based on the random forest (RFWQI) model in this study addresses these issues. It selects water quality indicators based on the actual pollution situation in the study area, employs an advanced random forest model to rank water quality indicators, determines indicator weights using the rank centroid method, scores the indicators using a sub-index function designed for groundwater development, and compares the results of two commonly used aggregation functions to identify the optimal one. Based on the aggregated scores, the water quality at 137 monitoring sites is classified into five levels: “Excellent”, “Good”, “Medium”, “Poor”, or “Unacceptable”. Among the 11 water quality indicators (sodium, sulfate, chloride, bicarbonate, total dissolved solids, fluoride, boron, nitrate, pH, COD_Mn, and hardness), chloride was given the highest weight (0.236), followed by total dissolved solids (0.156), and sodium was given the lowest weight (0.008). The random forest model exhibits a good prediction capability before hyperparameter tuning (86% accuracy, RMSE of 0.378), and after grid search and five-fold cross-validation, the optimal hyperparameter combination is determined, further improving the performance of the random forest model (94% accuracy, F1-Score of 0.967, AUC of 0.91, RMSE of 0.232). For the newly developed groundwater sub-index function, interpolation is used to score each indicator, and after comparing two aggregation functions, the NSF aggregation function is selected as the most suitable for groundwater assessment. Overall, most of the groundwater in the study area was of poor quality (52.5% of low quality) and not suitable for drinking.

Groundwater contamination risk assessment is an important basis to support the protection of the groundwater ecological environment. In this research, the groundwater contamination risk in typical karst areas in China was evaluated by PLEIK model (P: protective cover; L: land use; E: epikarst development; I: infiltration conditions; K: karst development) and classification and discrimination methods were used to assess groundwater vulnerability and pollution source load; the water quality index method was used to assess the status of groundwater contamination in the research area. The results show that groundwater vulnerability values in the research area range from 3.04 to 7.79, and most areas have low groundwater vulnerability. Groundwater pollution loading indexes, in the region of the pollution sources, gathered numerical up-water quality status evaluation that shows that most of the regional groundwater quality in the research area has good groundwater at present. The groundwater contamination risk assessment results show that the groundwater pollution risk is mainly at a very low level in most areas of the study area, but the groundwater pollution risk is higher in the areas where the pollution sources gather. The result reveals that the regional groundwater contamination risk level for regional groundwater ecological environment protection provides a theoretical basis for policy making.

This paper takes a portion of the Manas River Basin in Xinjiang Province, China, as an example and proposes an improved traditional comprehensive water quality index (WQI) method using Extreme Gradient Boosting (XG-BOOST) to analyze the groundwater quality levels in the region. Additionally, XG-BOOST is used to screen the existing dataset of ten water quality indicators, including fluoride (F), chlorine (Cl), nitrate (NO), sulfate (SO), silver (Ag), aluminum (Al), iron (Fe), lead (Pb), selenium (Se), and zinc (Zn), from 246 monitoring points, in order to find the dataset that optimizes model training performance. The results show that, in the selected study area, water quality categorized as “GOOD” and “POOR” accounts for the majority, with “GOOD” covering 48.7% of the area and “POOR” covering 31.6%. Regions with water quality classified as “UNFIT” are mainly distributed in the central–eastern parts of the study area, located in parts of the Changji Hui Autonomous Prefecture. Comparatively, water quality in the western part of the study area is better than that in the eastern part, while areas with “EXCELLENT” water quality are primarily distributed in the southern parts of the study area. The optimal water quality indicator dataset consists of five indicators: Cl, NO, Pb, Se, and Zn, achieving an accuracy of 98%, RMSE = 0.1414, and R² = 0.9081.

Based on the groundwater chemistry data from Karamay City, Xinjiang Province, this study examines seven major components: K⁺, Ca²⁺, Na⁺, Mg²⁺, SO₄²⁻, Cl⁻, and HCO₃⁻. The sampling was conducted during two periods: flood and dry periods. On the basis of analyzing the regional geologic background and hydrogeologic conditions, the study area was divided into calculation units and the test data validity was screened. Then, the outliers were eliminated by Grubbs’ method and Piper’s trilinear plot method, and the effectiveness of the elimination was evaluated by the box-and-line diagram. After that, the distribution types of the groundwater chemistry data in each calculation unit were judged to obtain the background values of the seven test indexes for the groundwater in different calculation units. The results show that Grubbs’ method and Piper’s trilinear plot method are effective in removing outliers. Secondly, the background values of Na⁺ and Cl⁻ in the groundwater of Karamay City are mostly higher than those of other anions and cations during the two sampling periods. This phenomenon may be due to the concentration effect of evaporation. Additionally, the groundwater background values of Ca²⁺, Na⁺, and Cl⁻ showed more significant differences between the two sampling periods compared to other ions.