# Innovative Approaches for Minimizing Disinfection
## 5 Machine Learning Approaches  
Machine learning (ML) is a specialized discipline within computer science and artificial intelligence that focuses on developing machines that learn from data and make predictions without explicit programming. In the field of ML, the learning process entails utilizing statistical analysis to examine input data and generate predictions. In most cases, ML refersto particular algorithms that enhance themselves to analyze large datasets. The efficiency of developed algorithms is directly proportional to the size of the dataset. Algorithms for ML are becoming more and more useful for a variety of uses. ML approaches are applied in several research topics, including analytical chemistry [45; 46], ophthalmology [47], the food and agricultural sector [48], psychology [49], water quality control [50], and many more. [PERSON] et al. [45] established and suggested an ML approach and demonstrated its robustness and value as a tool for scientists, researchers, engineers, and practitioners in monitoring water systems and designing wastewater treatment plants. Analytical chemistry has undergone a radical transformation because of ML, which has profoundly altered how data assessment is evaluated and understood. The advancements in this subject have greatly improved the capacity to use various statistical and mathematical tools, also known as algorithms, to extract useful information, identify patterns, and make precise forecasts. Many water distribution networks face challenges when related to monitoring emerging DBPs. Utilizing empirical models to forecast challenging-to-measure water quality parameters may offer a viable alternative to traditional water sampling and analysis [51; 52]. Empirical models can be utilized to forecast the contents of specific contaminants (known as response) by easily accessible data on other factors related to water quality and operational conditions (known as predictors). A proposed tackle could be the use of machine learning-based predictive modeling. To create machine learning models that can forecast DBPs, it is crucial to have access to extensive datasets that include information on water quality. The datasets should include ample water quality information to facilitate an analysis of the intricate correlation between predictors and the response. However, gathering information on newly formed DBPs in water distribution networks can be challenging at times, and the creation of ML models often encounters restrictions due to limited data availability [53]. Recent studies [50; 54; 55] have indicated that various machine learning techniques, such as regression trees, support vector regression (SVR), and Gaussian process regression (GPR), can effectively monitor emerging contaminants in water distribution networks and optimize disinfection operations to reduce the occurrence of these contaminants. Hence, assessing the efficacy of various ML methods in forecasting and developing DBPs while dealing with limited datasets is valuable. According to [PERSON] et al. [56], modern ML techniques might exhibit more accuracy than linear or log-linear power functions when forecasting the development of DBPs. Many researchers [56; 57; 58; 59] have demonstrated that ANNs outperformed multivariate power functions in forecasting the generation of DBPs. Nevertheless, using other machine learning techniques to predict the development of disinfection byproducts (DBPs) is infrequently documented [56]. [PERSON] et al. [56] specifically constructed a prediction model of chloral hydrate, effectively addressing the existing gaps in information. The random forest regression and support vector regression algorithms outperformed the classic stepwise multiple linear regression approach in each DBP species' training and testing sets. This was seen by their lower RMSE and MAPE values, as well as their higher R2 values.