## 1 Introduction
The most accurate results were achieved with ensemble models based on decision trees, specifically XGBoost and LightGBM. The recall metric reached values of 0.83 at various stages of modeling. The authors conclude that one direction for advancing classification modeling approaches in wastewater treatment plants is to focus on domain distribution characteristics. [PERSON] [23] conducted an analysis of 23 models for the effectiveness of classifying wastewater treatment quality. It is worth noting that the quality of treatment was classified based on individual parameters, resulting in a binary classification of \"High\" and \"Low\". This approach appears somewhat unusual, as a regression model could have been employed for the same purposes. Nonetheless, the available predictors were tested using classical classification models, including k-nearest neighbor (KNN), support vector machine (SVM), and decision trees (DT). The classification accuracy reached 88%; however, given that the classes were unbalanced, it would be prudent to assess additional classification metrics. A similar study was conducted by [PERSON] [24], who also addressed the classification task to predict the quality of treated wastewater. Interestingly, the training dataset consisted of data from various treatment plants. One of the models used, CatBoost, demonstrated the highest effectiveness, achieving a classification accuracy of 94.51%. CatBoost is a multi-platform machine learning library developed by Yandex (Yandex, Moscow, Russia), designed for implementing gradient-boosting methods. Currently, it is one of the most popular models in data analysis across various scientific fields. Interestingly, in the articles reviewed in recent years concerning wastewater treatment, CatBoost models are rarely encountered. Nevertheless, among the advantages of these models are enhanced support for categorical features with automatic encoding, resilience to overfitting, rapid training times, versatility (the ability to address regression, classification, and ranking tasks), interpretability, and support for various platforms. CatBoost demonstrates high speed and accuracy, which contribute to its growing popularity in machine learning project applications [25; 26; 27; 28]. Many modern approaches to wastewater parameter modeling rely on deep neural networks. These include Recurrent Neural Networks (RNN) and their variants, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which are widely used for time series forecasting and sequential data, making them especially useful for monitoring wastewater parameters (e.g., BOD5, COD, TSS) over time. These networks can capture temporal dependencies, which is critical when analyzing seasonal or short-term fluctuations in wastewater characteristics. However, they may encounter difficulties in handling long-term dependencies [29]. Transformers, such as Temporal Fusion Transformers (TFT), are well-suited for modeling complex temporal dependencies and can capture relationships among various wastewater characteristics. They are particularly useful for multivariate data when several wastewater indicators (e.g., BOD, COD, pH, TSS) need to be considered simultaneously in forecasting. These models support variable and temporal dependency analysis in complex, highly variable data [30; 31]. Neural networks based on N-BEATS (Neural Basis Expansion Analysis for Time Series Forecasting) represent a relatively new architecture for time series forecasting, enabling the effective modeling of long-term dependencies without using recurrent blocks. This architecture is suitable for predicting complex trends and analyzing temporal data, especially when seasonality and trends are less pronounced, as can occur in wastewater [32; 33]. Combinations of CNNand RNN (e.g., CNN-LSTM) or GRU with autoencoders, such as top-sparse autoencoders (TSA), are used for processing and forecasting high-dimensional wastewater data.