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Machine Learning Tools for Water Resources Modeling and Management  

Giorgio Guariso and Matteo Sangiorgio

The pervasive diffusion of information and communication technologies that has characterized the end of the 20th and the beginning of the 21st centuries has profoundly impacted the way water management issues are studied. The possibility of collecting and storing large data sets has allowed the development of new classes of models that try to infer the relationships between the variables of interest directly from data rather than fit the classical physical and chemical laws to them. This approach, known as “data-driven,” belongs to the broader area of machine learning (ML) methods and can be applied to many water management problems. In hydrological modeling, ML tools can process diverse data sets, including satellite imagery, meteorological data, and historical records, to enhance predictions of streamflow, groundwater levels, and water availability and thus support water allocation, infrastructure planning, and operational decision-making. In water demand management, ML models can analyze historical water consumption patterns, weather data, and socioeconomic factors to predict future water demands. These models can support water utilities and policymakers in optimizing water allocation, planning infrastructure, and implementing effective conservation strategies. In reservoir management, advanced ML tools may be used to determine the operating rule of water structures by directly searching for the management policy or by mimicking a set of decisions with some desired properties. They may also be used to develop surrogate models that can be rapidly executed to determine the optimal course of action as a component of a decision-support system. ML methods have revolutionized water management studies by showing the power of data-driven insights. Thanks to their ability to make accurate forecasts, enhanced monitoring, and optimized resource allocation, adopting these tools is predicted to expand and consistently modify water management practices. Continued advancements in ML tools, data availability, and interdisciplinary collaborations will further propel the use of ML methods to address global water challenges and pave the way for a more resilient and sustainable water future.

Article

Wastewater Reclamation and Recycling  

Soyoon Kum and Lewis S. Rowles

Across the globe, freshwater scarcity is increasing due to overuse, climate change, and population growth. Increasing water security requires sufficient water from diverse water resources. Wastewater can be used as a valuable water resource to improve water security because it is ever-present and usually available throughout the year. However, wastewater is a convoluted solution because the sources of wastewater can vary greatly (e.g., domestic sewage, agricultural runoff, waste from livestock activity, and industrial effluent). Different sources of wastewater can have vastly different pollutants, and mainly times, it is a complex mixture. Therefore, wastewater treatment, unlike drinking water treatment, requires a different treatment strategy. Various wastewater sources can be reused through wastewater reclamation and recycling, and the required water quality varies depending on the targeted purpose (e.g., groundwater recharge, potable water usage, irrigation). One potential solution is employing tailored treatment schemes to fit the purpose. Assorted physical, chemical, and biological treatment technologies have been established or developed for wastewater reclamation and recycle. The advancement of wastewater reclamation technologies has focused on the reduction of energy consumption and the targeted removal of emerging contaminants. Beyond technological challenges, context can be important to consider for reuse due to public perception and local water rights. Since the early 1990s, several global wastewater reclamation examples have overcome challenges and proved the applicability of wastewater reclamation systems. These examples showed that wastewater reclamation can be a promising solution to alleviate water shortages. As water scarcity becomes more widespread, strong global initiatives are needed to make substantial progress for water reclamation and reuse.