# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 5 Results
### Comparative Analysis of ML Methods
#### 5.1.1 Applications of Supervised ML Techniques in the Optimization of Treatment Processes  
Facilities that treat wastewater have increasingly turned to artificial intelligence to run more efficiently and to try to predict how clean their treated water will be. These plants use models (like artificial neural networks, support vector machines, and random forests) to understand the complex relationships between all the different factors that affect their treatment process [89]. Support vector machines offer another powerful way of dealing with large amounts of data and intricate relationships. They have seen success in both forecasting the quality of effluent and in adjusting the treatment process to achieve much better results [76]. Meanwhile, random forests have become quite popular because they are durable and can work with all sorts of information, whether numerical data or categorical data. Wastewater treatment plants are creating soft sensors, intelligent monitoring systems that employ machine learning to gauge vital measurements without the more expensive gear usually required. These digital sensors are placed inside the plant's control system (SCADA), letting operators use the new prediction capabilities to better tune the plant in real-time [12]. Figure 9: Supervised and unsupervised ML methods used in wastewater treatment. Intelligent systems learn from historical plant data, so they forecast the treatment process's behavior. RNNs and LSTM networks are particularly good at spotting immediate patterns and long-term trends in data. They are suitable for predicting a treatment system's critical water quality measures [12; 13; 80; 90; 91]. Intelligent treatment plants assess water quality and determine whether it is clean enough to reuse for purposes such as irrigation or industrial processes [27]. They predict not just how well the plant will work but also enable real-time monitoring, allowing for quick decisions that lead to stable operations and less environmental damage [12; 92]. Those are the plants' smart parts. When the intelligent treatment plant uses historical data, it achieves excellent performance at predicting when equipment is going to break down. These advanced prediction tools allow treatment plants to manage resources, use energy more efficiently, and meet environmental regulations more easily. Systems that learn from years of historical data become incredibly skilled at forecasting with the kind of accuracy that tools without this kind of data just cannot achieve when figuring out if and when equipment might break down; using the kind of data that plants have accrued over the years makes the predictions that much better [34; 63; 93]. The ubiquitous pumps, motors, valves, and other pieces of plant equipment are just as likely as any other to fail in ways harmful to the treatment process [59; 94]. The plants produce much data through equipment, control process sensors, and smart meters. The quantity of data generated by the plants is vast, and they are well akin to the sorts of information inherent in the IoT and Industry 4.0 [10]. Tables 5-9 concern methods of supervised machine learning that require labeled training data (input-output pairs) for predictive modeling in wastewater treatment. This forecasting technique is primarily used for predicting the effluent quality parameter metrics, estimating process performance metrics, and optimizing operational control strategies in WWTPs. Of course, this is among the simplest applications of machine learning within the WWT sector. A range of techniques from traditional neural networks and support vector machines to advanced Deep Learning Architectures (DLAs), hybrid models, and optimization-enhanced modeling techniques are used.