AI-based Models for Predicting Wastewater Effluent Quality

The precise prediction of wastewater effluent quality is very important for improving wastewater treatment plant performance. Due to the complex and nonlinear nature of wastewater treatment, traditional models often not provide reliable predictions. As a result, artificial intelligence methods have been increasingly used as alternative modeling tools. This paper reviews AI models which predict wastewater effluent quality from past research studies. It covers common methods, including artificial neural networks (ANN), support vector machines (SVM), random forest (RF), gradient boosting techniques, and deep learning models like long short-term memory (LSTM) networks. We check model performance using well-known measures, including root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Key factors affecting prediction accuracy such as input variable selection, data availability, data preprocessing, and specific characteristics of treatment processes are summarized. The review demonstrates that AI models perform differently in various settings because no single method works best in all situations. The aim of this study is to help select appropriate models and to show research gaps for future studies.

کلیدواژه ها:

Wastewater Treatment ، Artificial Intelligence (AI) ، Effluent Quality Prediction ، artificial neural networks (ANN) ، support vector machines (SVM) ، random forest (RF) ، long short-term memory (LSTM)