A critical review of statistical, signal processing and machine learning methods for continuous and high-frequency water quality data improvement

In the digital water world, high-frequency water quality monitoring from sensors is crucial for capturing rapid changes, especially during storm events or discharge fluctuations, in which important signals can occur at sub-hourly intervals. These signals are represented in a time series and can sometimes be irregular, noisy, and prone to missing values or errors due to buried conditions, sediment interference, and signal loss. The fine resolution of reporting also increases the risk of sensor errors and data loss, necessitating effective correction methods to ensure the accuracy and usability of the data. This literature review investigates the current state of time series data correction and denoising techniques in water quality monitoring. A systematic review of peer-reviewed studies was conducted to identify commonly applied methods, evaluate their effectiveness, and assess their adaptability to high-frequency, nonlinear, and non-stationary water quality datasets. The study explored techniques, including statistical methods such as moving averages, median filtering, Savitzky-Golay smoothing, wavelet transforms, and Kalman Filter, as well as machine learning models such as random forest, support vector machine and gradient boosting. While many of these methods are well established in other fields, this review collates evidence of their application and adaptation to water resources. This review serves as a comprehensive resource for researchers and water resource practitioners to implement appropriate denoising and correction techniques for continuous and high-frequency monitoring data. It highlights the potential of both statistical, signal processing, and machine learning-based methods to support accurate analysis, decision-making, and long-term water quality monitoring, management, and modeling. • Provides an extensive background on high-frequency water quality datasets. • Discusses strengths and limitations of over 15 water quality improvement techniques. • Demonstrates the application of unique data improvement algorithms. • Emphasizes the need for uncertainty quantification and real-time deployment.