# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 4 Classification of ML Models Applied in the Optimization of Wastewater Treatment Processes
### ML Models Applied in the Optimization of Wastewater Treatment Processes
#### 4.2.5 Hybrid Deep Learning (HDL) Approaches  
HDL approaches that combine DL with optimization or statistical models have been explored in order to improve the model's prediction accuracy and to increase the model's robustness in different domains (such as wastewater treatment processes optimization), being a useful tool for dimensionality reduction and feature extraction in high nonlinearity handling (DL combined with Partial Least Squares Regression--PLSR). A hybrid model that combines the advantages of CNNs (feature extraction ability) and LSTM networks (temporal sequence learning) with GPR (interval prediction strength) was used in order to predict water quality parameters, such as conductivity and total dissolved solids (TDS), demonstrating superior performance compared with the traditional ML methods [42]. Combining the advantages of CNNs with those of LSTM networks proved to supply superior prediction accuracy for essential water parameters (such as dissolved oxygen levels) compared with traditional ML models, like Support Vector Regression (SVR) and DTs [28]. Additionally, the combination of DL with EM methods, such as RNNs with RFs, was proved to maximize the method's predictive abilities (by capturing temporal dependencies and feature relationships), with benefits regarding the model's accuracy and robustness for environmental monitoring tasks [26]. The usage of hybrid models highlights the potential of integrating DL with optimization algorithms and statistical models in order to overcome prediction challenges,supplying a flexible and powerful tool for different applications. An example in this sense is the CNNE-LOST-GPRE (Convolutional Neural Networks-Long Short-Term Memory Neural Networks-Gaussian Process Regression) model, which uses an optimization algorithms (rat swarm algorithm) to improve the accuracy and adaptability of these hybrid models, to achieve superior performance in predictive tasks, making these models more suitable for real time complex and nonlinear applications [42]. Recently, there has been a successful development in fuzzy neural networks for water quality prediction, and interval type-2 fuzzy neural networks show better predictive performance considering adaptive membership functions [85]. Statistical techniques (such as cross-validation and grid search) and statistical metrics (Root Mean Square Error--RMSE, Mean Absolute Error--MAE) are also integrated in DL methods in order to streamline the hyper parameter fine tuning process; reduce overfitting risk; select the most effective model; ensure high accuracy in complex prediction tasks; and to obtain more accurate, reliable, and interpretable models necessary for water quality prediction and for environmental monitoring [28; 29; 58]. This combination of DL models with statistical ones also facilitates the development of models that are capable of handling the complex relationships of real-world data, models that are computationally efficient [86]. In Table 4, the main DL models' advantages, disadvantages, and applications in the optimization of wastewater treatment processes are presented.