Machine Learning Approaches to Optimize Water Management in the Tagus-Segura Aqueduct System

The Tagus-Segura aqueduct (TSA) is a strategic water transfer scheme and the largest hydraulic infrastructure in Spain. It consists of a 286 km-long pipeline that connects the Bolarque reservoir, in the Tagus River Basin, to the Talave reservoir, in Segura River Basin, which is one of the most water-stressed Mediterranean basins.

To ensure a sustainable management of the system, a series of water transfer rules were created, that establish the monthly transferred volume according to the total water volume stored in the Entrepeñas and Buendía reservoirs, located in the headwaters of the Tagus River Basin, and the inflows to these reservoirs in the previous twelve months.

Artificial intelligence methods, such as Artificial Neural Networks (ANN), have become very popular in streamflow forecasting applications due to their simple implementation, low requirement of hydrological data and good prediction performance. Accurate and reliable streamflow forecasting may have a significant impact on water resources management, especially for reservoir operation optimization.

This work focuses on the development of ANN models to predict the monthly inflows to the Entrepeñas and Buendía reservoirs. For each of the reservoirs, multi-layer perceptron ANN with backpropagation were trained, using monthly historical data of the inflows and precipitation. To identify the model with the best performance, various tests were conducted involving different combinations of hyperparameters, as well as varying sets of explanatory variables. The models were evaluated using the Nash-Sutcliffe Efficiency (NSE) coefficient to assess their predictive accuracy, in each of the subsets: training, validation and testing.

The best fit was achieved by incorporating several lags from the original series along with precipitation data including a single lag. This combination resulted in a fit to the full series with NSE values exceeding 0.7 for the inflows to both reservoirs.

These models could be used to support the management of water resources in the TSA system. By identifying future trends in water resource availability, decision-makers can implement more efficient strategies to optimize water allocation, ensure sustainability, and mitigate the effect of potential droughts.