With the fast growth of aquatic data, machine learning is essential for data analysis, categorization, and prediction. Data-driven models using machine learning may effectively handle complicated nonlinear problems in water research, unlike conventional approaches. Machine learning models and findings have been used to build, monitor, simulate, evaluate, and optimize water treatment and management systems in water environment research. Machine learning may also enhance water quality, pollution control, and watershed ecosystem security. This chapter discusses how ML approaches were used to assess water quality in surface, ground, drinking, sewage, and ocean water. The authors also suggest potential machine learning applications in aquatic situations.
Top2. Machine Learning Overview
ML is commonly utilized to find insights or have predictions from vast data from many contexts. Prior to using ML, data collecting, algorithm selection, model training, and validation are needed. Among these methods, selecting algorithm is the key aspect.
Figure 1. Water systems employ machine learning extensively. WWTP, wastewater treatment facility.
Machine learning has two primary classes: supervised and unsupervised. Labels in datasets distinguish these two kinds. Supervised learning predicts from labeled training datasets. Input and anticipated output values are included in each training instance. Supervised learning algorithms discover input-output correlations and create a predictive model to estimate the outcome from the I/P data. Supervised learning methods, such as LR, ANN, decision trees, SVM, Naive Bayes, KNN, and random forests are designed for data classification and regression.
In contrast, unsupervised learning handles data generally without labels, addressing pattern recognition problems using unlabeled training datasets. Unsupervised learning classifies training data depending on features, primarily via dimensionality reduction and grouping. But, the quantity and significance of categories are unknown. Thus, unsupervised learning is often employed for classification and association mining. PCA, K-means, and other unsupervised machine learning methods are popular. Reinforcement learning, which allows machines to generalize and solve unlearned problem is a different kind of ML method. In contrast to the other 2 ML classes, it is sometimes used in the aquatic aspect.
Top3. Ml In Various Water Applications
Various researchers have utilized ML to solve water treatment and management system problems (Fig. 2), like real-time tracking, estimation, pollutant source estimating, concentration prediction, water resource allotment, and technology optimization.