## 3 Using ML for Water Activities  
From the late twentieth century to the present day, the rate of land use and land cover change (LULC) has been steadily increasing, primarily driven by uncontrolled population growth and economic and industrial development, especially in developing countries [210]. Desirable quality and quantity of water resources are essential for human survival and sustainable development. With a continuous increase in population, the demand for water is rising in tandem. ML plays a crucial role in managing, interpreting, and analyzing water resources. ML can predict water quality, map groundwater contaminants, classify water resources, detect contaminant sources, assess contaminant toxicity in natural water  
Figure 2: PRISMA analysis flowchart for the bibliometrics of the “machine learning methodologies”. systems, model treatment techniques, aid in characterization analysis, facilitate drinking water purification and distribution, and assist in wastewater collection and treatment in engineered water systems [211]. Determining and managing water quality are crucial for human wellbeing, but challenges arise from human errors. ML applications have become important in facilitating these processes, as demonstrated by successful integration into public systems and detailed investigations on shared datasets in water research [212]. Water resources management planning emphasizes the importance of careful planning and forecasting hydrological parameters like rainfall, runoff, solar radiation, groundwater, and evaporation for effective water resource management [213]. Mapping of the area of interest is carried out by applying remote sensing [214]. Through remote sensing, crop disaster monitoring, urban planning, and water resource management can be achieved. The creation of a thematic map from the satellite images of the area is achieved by image classification. In order to perform water detection from satellite images, software must be developed that uses multiple spectral bands to enable parallel image classification using supervised or unsupervised learning. The information obtained from the analysis can be used in protected natural areas to monitor water behavior over time [214]. High-resolution remote sensing techniques, also through big data and ML technologies, have the potential to impact many aspects of environmental and water management (EWM). Based on these, weather forecasting, disaster management, and the creation of smart water and energy management systems can be achieved. ML, like DL, tries to simplify as much as possible the difficulty of interpreting big data, due to the huge amount of information, by developing powerful algorithms and thereby extracting hierarchical features from the data [215]. Algorithms such as random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive supervised coordination theory and predictive mapping (fuzzy ARTMAP), spectral angle mapper (SAM), and Mahalanobis distance (MD) are capable of evaluating the accuracy through the use of kappa coefficient, RoC, and root mean square error (RMSE) [210]. Exploration of the potential applications of big data in the field of water resource engineering is the central focus. This inquiry encompasses an in-depth analysis of the merits and demerits associated with the utilization of big data techniques. Additionally, a succinct overview of the pertinent literature and empirical case studies is provided. Ultimately, it is postulated that the adoption of big data methodologies has the capacity to significantly enhance the precision and effectiveness of engineering solutions within the domain of water resources. The proposal of implementing a platform-based big data system for sharing critical data is highlighted, enabling the production of high-value and reliable information through the rapid processing of large and diverse datasets.