Applying AI and Remote Sensing to Water Resource Management: A Case Study in Almada, Portugal

Water availability faces difficulties in balancing supply and demand. Factors such as climate change are causing a shortage of this resource, and the growing population is increasing the demand for it. Urban landscapes represent around 70% of urban water use and since they are privately owned, they are often difficult to access for data collection. This gap drives the need for new methods of estimating outdoor water consumption, since it can be a crucial input for decision-making in water distribution systems. This paper proposes the use of Remote Sensing (RS) and Artificial Intelligence (AI) models to collect data on the consumer's outdoor water usage and tackle the problem of non-revenue water. In partnership with SMAS Almada, the aim is to study an area where apparent water losses are very high in order to help identify the specific location in the network where they occur and who may be responsible for them. To reach this goal, two YOLO models were created to detect and segment properties in satellite images. Using these models as a basis, a Water Consumption Pipeline (WCP) that could associate the coordinate of each property to its estimated water consumption, was developed. Employing the WCP, a database regarding the study area and a platform where users can visualize the estimated water consumption of the properties in a specific geographic coordinate were also created. This research provides a solution designed to help reducing water losses in critical areas and enhances water resource management by providing SMAS Almada with valuable data to improve asset supervision. Additionally, combining the information on billed water with the predicted usage, it is possible to perform a discrepancy analysis and gain insight on how the consumers are using water in residential areas.