## 2 ML Methodologies
### Reinforcement Learning
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Algorithm** & **Description** & **Applications** & **Ref.** \\ \hline Machine learning and water resources management & Co-training & Enhances water quality prediction models by leveraging data from multiple sources, such as remote sensing and in situ measurements. & Water quality prediction, data fusion from diverse sources. & [175] \\ \hline Data mining and water quality assessment & Clustering & Clusters water quality data to identify patterns and anomalies for improved monitoring and assessment. & Water quality analysis, anomaly detection in sensor data. & [176] \\ \hline Machine learning and environmental monitoring & Self-training clustering & Utilizes unlabeled water quality data to improve clustering algorithms for water quality assessment. & Water quality clustering, unsupervised anomaly detection. & [177] \\ \hline Data integration and environmental assessment & Constrained clustering & Applics constraints to clustering algorithms to account for domain knowledge in water quality analysis. & Water quality clustering with domain- specific constraints. & [178] \\ \hline Semisupervised learning and environmental data & Semisupervised & Combines labeled and unlabeled environmental data to improve water quality modeling and assessment. & Water quality prediction, leveraging partial labels. & [179] \\ \hline Statistical modeling and environmental data & Expectation maximization (semi-EM) & Uses the expectation maximization algorithm to estimate parameters in semisupervised water quality models. & Parameter estimation in semi-supervised models. & [180] \\ \hline Machine learning and environmental data & Co-EM clustering & Integrates expectation maximization and clustering for semisupervised water quality analysis. & Semisupervised clustering in water quality assessment. & [181] \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Semisupervised clustering algorithms for water resources management. In RL, an agent interacts with its environment, perceiving and interpreting its state, taking actions, and receiving feedback in the form of rewards or penalties [183]. Through repeated interactions and learning from trial and error, the agent improves its decision-making abilities and optimizes its behavior to achieve the highest cumulative reward over time. The scope of RL extends beyond traditional ML domains. It explores how both physical and artificial systems can learn to predict the consequences of their actions and optimize their behavior in dynamic environments [184]. RL finds applications in diverse fields such as ethology, economics, psychology, and control theory. It enables researchers to understand and simulate how organisms and systems adapt, learn, and make decisions based on the outcomes of their actions. By leveraging the principles of RL, researchers aim to develop intelligent systems that can autonomously learn and improve their performance in complex and uncertain environments. RL provides a framework for understanding and modeling the decision-making process, enabling machines to make informed choices and achieve superior performance in a wide range of applications. Table 18 provides an overview of reinforcement learning algorithms. These algorithms are central in the field of machine learning, as they are designed for training agents to make sequential decisions in dynamic environments. Table 19 shows connections between water management techniques and reinforcement methods, with practical applications and references for verification.