Water and energy demand forecasting in large-scale water distribution networks for irrigation using open data and machine learning algorithms

In a world where the availability of water is decreasing, its use must be thoroughly optimized. Irrigated agricultural systems, as the main user of the planet's fresh water, must improve its management and save as much of this scarce resource as possible. However, the heterogeneity of these complex systems that are frequently organized in water user associations makes the daily management of this resource difficult. The new information and communication technologies as well as artificial intelligence techniques help to understand the heterogeneity of these complex systems, making it possible to better manage them. However, the implementation of a tool with these characteristics requires a large and heterogeneous amount of data from different sources. Thus, in this work, a new tool for managers based on water demand forecasting at the field scale for the week ahead has been developed. This tool, WatergyForecaster, combines artificial intelligence techniques, satellite remote sensing (Sentinel 2) and open source climate data to automatically build a water forecasting model at the farm scale for a week in advance. WatergyForecaster, developed in Python, was applied to a real water user association (WUA), obtaining a set of optimum models with an accuracy that ranged from 17% to 19% and representativeness higher than 80%.