# Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
## 1 Introduction  
Water quality datasets often exhibit challenges such as non-normal distributions, outliers, missing values, low concentrations below detection thresholds, and serial dependence. To ensure the accuracy and reliability of predictive models in water qualityanalysis, appropriate statistical methodologies must be employed. A range of statistical techniques is available to enhance the robustness, efficiency, and interpretability of water quality prediction models. These techniques are critical for evaluating model performance, diagnosing potential issues, and ensuring reliable predictions when applied to real-world datasets. Among the commonly used statistical procedures are graphical analysis, trend analysis, correlation analysis, regression analysis, and time series analysis [1]. Visualization techniques, including box plots, scatterplots, and Q-Q plots, are particularly useful in identifying patterns, anomalies, and distributional properties within datasets [2; 3]. Recent advancements in water quality monitoring have emphasized the application of statistical analysis and emerging technologies to enhance environmental assessment. For example, [PERSON] et al. [4] conducted a comprehensive bibliometric analysis on the integration of machine learning in wastewater treatment, highlighting global research trends and the crucial role of statistical data interpretation in optimizing water treatment processes. Similarly, [PERSON] et al. [5] presented a real-time seawater quality monitoring system in Cyprus, combining statistical methods with sensor networks to enable prompt environmental management responses. In another study, [PERSON] et al. [6] utilized combined absorption and fluorescence spectroscopy with statistical spectral analysis to improve dissolved organic carbon monitoring in seawater, demonstrating the reliability of such approaches in marine research. In parallel, [PERSON] et al. [7] applied Top2 Vec topic modeling and statistical data analysis to evaluate publication dynamics in environmental monitoring using unmanned aerial vehicles, showcasing how bibliometric and statistical tools can support technological innovation tracking in water quality control. Additionally, [PERSON] et al. [8] explored the ecological impacts of glyphosate herbicide on seagrass species, employing extensive statistical data interpretation to assess water quality degradation in Florida estuaries, underscoring the significance of statistical rigor in environmental risk assessment. These studies collectively illustrate the indispensable role of statistical analysis in both operational water quality monitoring and broader environmental research, supporting data-driven decision-making in aquatic ecosystem management. The application of machine learning, data assimilation, and statistical techniques has proven highly effective in addressing environmental challenges such as environmental retrieval, bias correction, and environmental monitoring. Bias correction methods and statistical calibrations have been integrated into environmental risk models to enhance prediction reliability and interpretability [9]. Further, advanced data-driven frameworks have been developed to assimilate multi-source environmental datasets and reduce uncertainties in climate and water quality forecasting [10]. These studies underline the importance of combining physics-based understanding with data-driven analytics to solve real-world environmental problems. Additionally, recent advances in model evaluation and hybrid frameworks illustrate the value of principal component analysis (PCA) and other statistical techniques in reducing data dimensionality and extracting meaningful patterns from multivariate environmental datasets [11]. The application of advanced computational techniques is significantly advancing environmental science. Research demonstrates that integrating improved remote sensing algorithms with data assimilation can enhance the retrieval and prediction of atmospheric pollutants [12].