Environmental decision support models based on deep learning

Environmental decision support models based on deep learning


Funded by UNICEN

The main goal of this project is to generate knowledge for environmental decision making based on the analysis of data provided by the flood and drought monitoring network in the Tandileofú river basin, developed by the IHLLA, through the application of Artificial Intelligence techniques, in which the ISISTAN group specializes. In particular, the project will generate predictive models of evapotranspiration using Deep Learning techniques on the remote sensing data coming from the monitoring network. The concrete goal of the project is to obtain soil moisture maps at the basin scale with a spatial and temporal resolution necessary for the adequate environmental management of the system based on predictive models generated with Deep Learning techniques. The aim is to generate information that allows to know (and forecast) the state of the superficial and underground courses of the application basin.

PIs: Daniela Godoy (ISISTAN) - Fabio Peluso (IHLLA)

Research team:

  • Diego Alonso (ISISTAN)
  • Luis Berdun (ISISTAN)
  • Adan Faramiñan (IHLLA)
  • Mauro Holzman (IHLLA)
  • Paula Olivera Rodriguez (IHLLA)
  • Juan Manuel Rodriguez (ISISTAN)
  • Silvia Schiaffino (ISISTAN)
  • Alfredo Teyseyre (ISISTAN)
  • Sebastian Vallejos (ISISTAN)


  • Adrián Guerrero
  • Cristian Laino Baldini
  • Facundo Galufa
  • Julia Naveyra
Daniela Godoy

My research interests include recommender systems, social networks, text mining and social networks.