Research progress on water quality prediction in wastewater treatment systems based on data-driven approaches

Traditional water quality monitoring methods are time-consuming, costly, and have poor data timeliness, leading to long feedback and adjustment cycles in wastewater treatment systems. With the rapid advancements in artificial intelligence, developing data-driven water quality prediction technologies is of significant importance. This study focused on the collection and processing of big data, the characteristics and applications of water quality data collection, cleaning strategies, and feature engineering methods were summarized. Based on this, the predictive performance and characteristics of different types of water quality prediction models were introduced. Statistical regression models, machine learning models, and deep learning models all demonstrated certain advantages. However, significant differences in data quality across different datasets made it difficult to obtain universal prediction models. By considering the features of big data and data quality, applying reasonable data preprocessing techniques, and utilizing various prediction methods or combinations, the accuracy of model predictions could be significantly improved. Finally, this review summarized the current state of water quality prediction models, existing challenges, and future development directions, aiming to provide a reference for the research, development, and application of water quality prediction models.