## 1 Introduction
However, it should be noted that the model's performance is highly dependent on the selection and quality of the predictors [4]. Regarding LSTM, this recurrent neural network has proven effective in time series forecasting, as demonstrated by [PERSON] [5], who applied it to predict two substrate concentrations in the synthetic ASM1 model, achieving a MAPE of 1.31%. Nevertheless, LSTM models have several limitations; they are prone to overfitting with small datasets and are sensitive to data outliers. Despite these challenges, many researchers currently approach the task of predicting wastewater treatment quality as a time series forecasting problem. [PERSON] [6] applied the Deep Learning Time Series Forecasting (DLTSF) approach to predict key parameters of treated wastewater at the El-Berka treatment plant in Egypt. The Root Mean Squared Error (RMSE) for total nitrogen was 1.92, which is considered a fairly accurate prediction. [PERSON] [7] presented a comprehensive model comparing the performance of various regression models--Artificial Neural Network (ANN), Fuzzy Logic (FL) algorithms, Random Forest (RF), and LSTM--for predicting treated wastewater parameters. The models were evaluated using metrics such as RMSE, MAPE, Mean Squared Error (MSE), and the Determination Coefficient (DC). The controlled parameters included organic pollutants, nutrients, suspended solids, and heavy metals. According to the reviewed sources, current models can predict treated wastewater quality with a MAPE of around 1.0%, which displays a remarkably high accuracy for machine learning models. The most accurate model identified was the Outlier Robust Extreme Learning Machine (ORELM), a modification of the standard Extreme Learning Machine (ELM), specifically adapted to handle significant data outliers. The study confirms the presence of numerous data outliers in wastewater treatment studies, which is associated with the high variability of the processes. When developing models, it is essential to implement measures that mitigate the impact of outliers on modeling results. ORELM has been increasingly used for optimizing drinking and wastewater treatment processes, likely due to the unique distribution characteristics of the input data [8; 9; 10; 11]. [PERSON] and [PERSON] [12] applied an ensemble method to predict 15 operational parameters of wastewater treatment plants, including biomass properties. The models were trained using data collected over a 10-year period. The authors proposed a six-stage framework in which subsequent parameters were predicted based on previously forecasted ones. According to the authors, the symmetric mean absolute percentage error (SMAPE) using this approach was 7.5%. Another advantage of the multi-stage approach is the reduction in the \"black box\" effect, which is common in other data-driven models. The models used in this study included Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Support Vector Regression (SVR). The authors raised an important issue regarding the development of feature generation principles in wastewater quality models. In this study, intermediate features related to the sludge dose (TSS) in various parts of the bioreactor were generated at different stages, though this alone was insufficient to significantly improve model quality. Simple sequential transformations of predictors increase the risk of model noise (or data leakage), which greatly reduces model performance using test data. However, using forecasted values for intermediate calculations can enhance model accuracy. In such cases, selecting the appropriate calculation model is crucial. For example, [PERSON] [13] integrated machine learning approaches into the calculations of the Activated Sludge Model #3 (ASM3). The authors aimed to address the limitations of the mechanistic model, particularly those associated with the high variability of incoming wastewater. A machine learning model was employed to predict the biodegradability of wastewater during the treatment of petrochemical effluents.