## 3 Using ML for Water Activities
The work underscores the necessity of establishing standard operating procedures to preclude and address computational errors and malfunctions inherent in big data applications [216]. Water has a direct relationship with energy and food. Modeling and analysis of the energy, water, and food (EWF) relationship is performed with static, deterministic models that facilitate decision making for well-behaved and predictable resource systems over time. These frameworks, however, are partially limited in their functionality since they do not take into account the exposure of systems to the dynamic nature of exogenous uncertainties and associated risks at the interface. Through reinforcement learning based on sequential decision making called Markov decision process (MDP), design and control can be applied that could help achieve adaptive systems under unstable conditions with the aim of maximizing economic output and improving their operational resilience [217]. Another crucial aspect to address is data control. The collected data may sometimes be incomplete or noisy, resulting in missing features. Therefore, additional data samples are required to extract useful supervised or unsupervised classification methods. This issue can be addressed by decomposing the signal to compute missing features (data augmentation), classifying noisy samples, and artificially generating new data samples (data augmentation). ML plays a vital role in applications such as brain-computer interfaces, epileptic classification of intracranial electroencephalographic signals, face recognition/verification,and water network data analysis [218]. In terms of the urban sector, the use of hot water in homes plays a dominant role in people's lives, especially in winter periods. In recent years, renewable energy sources, such as wind and photovoltaic generation, have been increasingly applied, which has led to some problems in power systems, such as the duck curve and unreliability due to environmental variability [219]. An effective solution to this problem is demand response (DR). Learning hot water usage behavior allows water heating systems to continuously adapt to stochastic demand and reduce energy consumption. Electric water heaters (EWHs) are considered ideal candidates for DR due to their ability to store energy. Through ML, several objectives can be achieved, such as the following: 1. To determine the state of the art for energy optimization and scheduling of EWHs by creating smart grids and smart buildings; 2. To be able to predict the stochastic behavior of domestic hot water (DHW) demand and explore the potential energy use reduction by an adaptive system-smart dynamic simulation system for hot water use. ML models, such as random forest, multilayer perceptron, long short-term memory neural network, and LASSO regression, can be used in both classification and regression [220]. Another problem that households have to face is frequent attacks on water supply facilities, because this way, water distribution systems (WDSs) are deregulated [221]. To address this problem, it is proposed that in addition to traditional solutions such as data encryption and authentication, it is proposed to detect attacks on WDSs to reduce the cases of disruption. The attack detection system should meet two critical requirements: high accuracy and near-real-time detection. To achieve these two requirements, we can use self-predictive and unsupervised algorithms for attack detection in the cyberphysical domain (CP). For high accuracy, heuristic adaptive self-predictive algorithms are applied for near-real-time decision making and detection sensitivity. Unsupervised algorithms attempt to detect the attacks to maintain high detection accuracy as much as possible using isolation forest [221]. Based on the problems mentioned above, the World Health Organization has taken actions on water safety plans (WSPs), which involve holistic assessment and risk assessment.