CIMNE - Climate Change

The main objective of the group is to solve complex engineering problems by applying machine learning techniques with data obtained from sensors and numerical models.

The main area of activity is the field of hydraulic works: dams, spillways and water supply networks. However, these same techniques have been applied in the analysis of geomaterials such as railway ballast or landslides.

The group has a strong background in the use of machine learning techniques in health monitoring of dams for anomaly detection and predictive maintenance.
At present, we are developing methodologies for the efficient quantification of uncertainty in complex problems, combining machine learning and advanced numerical methods. The group has a clear practical approach, and includes among its capabilities the development of customized user interfaces.

New areas of application for machine learning techniques include water quality prediction and wastewater disinfection through advanced oxidation processes.