Research Cluster
Machine Learning and Models in Hydro-Environmental Engineering
Contact point
Fernando Salazar
Academic Leaders
Fernando Salazar, Ernest Bladé
External Advisors
Francisco Chinesta, Manolis Papadrakakis
Research groups
- River Dynamics and Hydrologic Engineering (FLUMEN Institute)
Ernest Bladé Castellet - Machine Learning in Civil Engineering
Fernando Salazar González
Overview
Staff
Projects
Publications
The cluster combines numerical modeling, machine learning, and specialized laboratory facilities to address complex challenges in hydraulic and environmental engineering. It integrates the FLUMEN Institute of CIMNE and UPC, leveraging a specialized Laboratory of River Dynamics and Hydrological Engineering for on-site testing.
The Machine Learning and Models in Hydro-Environmental Engineering Research Cluster specializes in solving practical problems related to hydraulic and hydrological engineering through an innovative combination of physically based numerical models, data-driven machine learning approaches, and laboratory testing. The cluster includes FLUMEN’s specialized Laboratory of River Dynamics and Hydrological Engineering, which provides specialized experimental facilities for model scale testing of river dynamic and hydraulic problems.
Using shallow water hydraulic models for river dynamics and flood risk assessment, three-dimensional finite element models for structural and hydraulic analysis of dams, and cutting-edge machine learning techniques for monitoring data analysis and model enhancement, the cluster delivers comprehensive solutions to complex hydro-environmental challenges. The integration of these diverse methodologies allows for improved accuracy in hydrological and hydraulic modeling.
Beyond its core focus, the cluster applies its expertise to interdisciplinary areas such as railway ballast behavior, landslide prediction, optimization of advanced wastewater disinfection processes, and air quality forecasting. Through a balanced portfolio of research activities, consultancy services, training programs, and technology transfer initiatives, the cluster remains at the forefront of hydro-environmental engineering, contributing valuable solutions to both industry partners and the broader scientific community.
Ongoing projects
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Related news
CIMNE experts use Machine Learning to streamline reservoir risk assessment
Researchers at CIMNE's Machine Learning in Civil Engineering Research Group and the Flumen Institute have developed a new machine-learning based tool to classify off-stream reservoirs based on their potential risk of rupture. The work, carried out as part of the...
CIMNE’s new machine learning-based software improves dam structural safety
Researchers from the Machine Learning in Civil Engineering group at CIMNE have developed a new machine-learning based software to predict structural behaviour of dams, allowing for enhanced decision-making and minimizing safety risks of these critical infrastructures....