Treffer: A Review of Research on Intelligent Modeling Approaches for Rural Wastewater Quality Prediction.

Water scarcity has become a major global concern, and the utilization of wastewater resources is seen as a key solution to address the supply-demand imbalance. Compared to urban areas, rural wastewater treatment lags significantly behind due to underdeveloped infrastructure and complex water quality, posing a major constraint to the development of wastewater resource utilization. Mechanism-driven models struggle with the non-stationary data in rural wastewater, while data-driven models are becoming increasingly popular due to their ability to simulate non-linear relationships and adapt to changing conditions, making them an effective way to solve the existing challenges. This paper reviews the development of data-driven models for water quality prediction, analyzing the performance and application scenarios of three types of methods: traditional statistical models, machine learning models, and deep learning models. Additionally, through an in-depth discussion of these methods and an analysis of the specific challenges in rural wastewater management, we propose optimization directions for rural wastewater modeling, including multi-source data fusion, soft measurement methods, and the integration of data and mechanisms, to support the development of intelligent and efficient prediction systems. [ABSTRACT FROM AUTHOR]