## 1 Introduction
As a result of the study, two models were developed, MLR-ASM3 (based on multiple linear regression) and DF-ASM3 (based on decision forests). The hybrid model achieved MAPE values of less than 25%, which represents a relatively high performance compared to classical ASM3 modeling. This hybrid modeling serves as a practical example of combining \"white-box\" approaches with high-performance, data-driven computations. An important aspect of regression tasks in wastewater treatment is the optimization of energy consumption. [PERSON] [14] conducted a comparative analysis of 23 models for predicting energy consumption at wastewater treatment facilities in Melbourne. Optimizing energy consumption is a crucial task, as estimates suggest that approximately 7% of global electricity consumption is attributable to the pumping and treatment of wastewater [15]. The time lag used in the model was set to 1 day. The most effective model was the gradient-boosting model XGBoost, which achieved an RMSE metric of approximately 12%. It is noteworthy that the study was conducted using open data, thus the issues of data collection and preparation were not addressed. However, the challenge of obtaining a representative sample for training and validating the model remains one of the primary issues that must be addressed when employing machine learning approaches in the modeling of wastewater treatment processes. The issue of high-frequency data collection of wastewater characteristics is addressed in the work of [PERSON] [16]. This study focuses on machine learning algorithms for optimizing the operation of aeration equipment. A total of 35 input parameters were utilized, of which only a few (such as dissolved oxygen concentration) could be collected in real-time. Nonetheless, the optimization of equipment operation resulted in a 31% reduction in energy consumption, which is a significant achievement. Increasing the sample size across all predictors could further enhance this outcome. This issue is also discussed in the study by [PERSON] [17]. Various models, including Artificial Neural Networks (ANN) and the M5 model tree, were considered, yielding satisfactory metrics. However, despite attempts to fine-tune the models, the primary limitation for improving predictive quality is the insufficient amount of representative data for training. This lack of data is primarily associated with inadequate funding for the operation of treatment facilities. In many cases, automatic analytical sensors are absent at the plants, and the quality control of treated wastewater is performed manually. The issue of data collection in the absence of sufficient technical resources is addressed in the study by [PERSON] [18], who trained over 20 models primarily based on Artificial Neural Network (ANN) architecture. The achieved metric values were relatively high; however, the data collection and forecasting structure rely on time series analysis, which necessitates a large number of continuously operating sensors. Consequently, models that reduce noise were examined in conditions of unstructured input data and a significant number of missing values. The best results were obtained using the Multihead-Attention-Based Gated Recurrent Unit (MAGRU) model. The modeling of total nitrogen was conducted with a time lag of 3 h, corresponding to the adopted technological scheme for wastewater treatment. This model has been referenced in several studies, demonstrating a generally satisfactory forecasting quality [19; 20; 21]. The second prevalent task in the field of wastewater treatment is classification. In most cases, this involves anomaly detection and the prevention of emergency situations at treatment facilities. For instance, researchers led by [PERSON] [22] developed an approach for classifying anomalies in wastewater treatment plants utilizing Sequencing Batch Reactor (SBR) systems. They identified the most representative predictors and time cycles for modeling.