Applying neural networks combined with Monte Carlo simulation in dam operations to obtain operational, economic and environmental gains

The waste of potable water is a problem that affects a population’s supply and the environment, raising the need for studies focusing on the adoption of efficient actions and modern technological resources, such as artificial intelligence (AI), for sustainable water management. However, in the literature there are few studies on the operational, economic and environmental benefits of using AI in dam management. In addition, no study has been found on this topic addressing the Cantareira system, located in the metropolitan region of São Paulo, Brazil, which is one of the largest water supply systems in the world. This work presents an approach combining an artificial neural network and the Monte Carlo simulation method for floodgate control in the Cantareira system. Furthermore, parameters are explored that make the simulations of water collection and distribution more realistic. The results (root mean squared error (RMSE) = 0.076 and R 2 = 0.963) confirm the viability of using the proposed approach to minimize water waste and flood risks, as well as to increase efficiency in water resource management. Furthermore, this study advances the state of the art by presenting a set of operational, economic and environmental benefits directly associated with the adoption of AI in floodgate management.