## 3 Using ML for Water Activities
For proper water management and water quality, drinking water suppliers should also take the income of the residents into consideration. Many countries and regions lack case studies, legal requirements, and educational resources for WSPs, corresponding to widespread capacity deficiencies in the water sector. For this reason, a taxonomy of WSP training through ML is proposed [222]. Renewable energy sources have the potential to be converted into different types of energy. For example, solar energy can be converted into chemical energy in addition to thermal energy with the help of ML. ML contributes to the acquisition of detailed scientific knowledge about the underlying principles governing light-harvesting phenomena and can accelerate the fabrication of light-harvesting devices [223]. Supervisory control and data acquisition (SCADA) systems play an important role in providing remote access, monitoring, and control of critical infrastructure (CI), including power systems, water distribution systems, nuclear power plants, etc. The increasing interconnectivity, standardization of communication protocols, and remote accessibility of modern SCADA systems have massively contributed to the exposure of SCADA and CS systems to various forms of security. Any form of intrusive action on SCADA units and communication networks can create catastrophic consequences for nations due to their strategic importance to CS operations. Therefore, prompt and effective detection and classification of intrusions in SCADA systems hold great importance for the operational stability of national CSs. Through supervised learning techniques, intrusion solutions for SCADA can be found [224]. The challenges facing agriculture are multifaceted and multifactorial. Mainly, problems arise in crops due to the unpredictable nature of climate change, water, and pests [225]. To be able to maximize crop yields, appropriate assessments of microclimate parameters need to be implemented at the commercial scale for indoor and emission-free farming. This is achieved using Internet of Things (IoT)-based sensors. To select the appropriate model for microclimate parameter assessment using IoT sensors, a comparison is made between greenhouse crop production systems as well as the outdoor environment. Withthis analysis, a better environment for cultivation can be achieved and, thus, productivity can be increased. The supervised learning algorithm offers self-adjusting reference inputs based on the selected crop. Solar radiation, water vapor pressure deficit, relative humidity, temperature, and soil fertility are the raw data processed using the appropriate model. Also, various growth stages such as light conditions and timeframes are considered to determine the reference limits for categorizing the variation in each parameter. The microclimate parameters can be dynamically estimated using the Simulink model and IoT sensor nodes [225]. The models used for crop improvement, yield prediction, crop disease analysis, and water stress detection are random forest (RF), which is a supervised ML algorithm [226], decision trees, support vector machines, Bayesian networks, and artificial neural networks. These methods enable the analysis of soil, climate, and water regime, which are significantly involved in crop development and precision agriculture [227]. In water treatment, AI techniques like ANN, DNN, gradient boosting, and random forest regression have been used. Multivariate LSTM models generate valuable data, and data preprocessing methods like interpolation and anomaly detection are explored. The random forest regression algorithm excels, accurately predicting MIW parameters like Fe and acidity over 60 days. This underscores AI's role in optimizing water treatment and the need for rigorous statistical analysis in model development [228]. The prediction of removal of different types of water pollutants is also carried out using ML algorithms.