Research Fellow
School of Agriculture
Technological Educational Institute of Crete
Contact Information:
Τel: +306947841439
e-mail: daliakopoulos@hydromech.gr
Brief CV
Dr Ioannis Daliakopoulos is a graduate of the Department of Environmental Engineering of the Technical University of Crete (2003) and holds an MASc in Water Resources from McMaster University, Canada (2005), specialising in groundwater level forecasting using Artificial Neural Networks. He received his PhD from the Department of Environmental Engineering, Technical University of Crete (2011), with a thesis on flooding forecasting with the use of a meteorological radar.
Since 2005 he has worked as a research engineer in the Water Resources Management and Coastal Engineering Laboratory, Technical University of Crete, participating in 10 national and international research projects on water resources, climate change and ecosystem interactions. His main research interests include Water Resources Management, Extreme Weather Events (flooding, drought), Climate Change, and Data Mining from Remote Sensing and Ground-Based Observations.
Τel: +306947841439
e-mail: daliakopoulos@hydromech.gr
Brief CV
Dr Ioannis Daliakopoulos is a graduate of the Department of Environmental Engineering of the Technical University of Crete (2003) and holds an MASc in Water Resources from McMaster University, Canada (2005), specialising in groundwater level forecasting using Artificial Neural Networks. He received his PhD from the Department of Environmental Engineering, Technical University of Crete (2011), with a thesis on flooding forecasting with the use of a meteorological radar.
Since 2005 he has worked as a research engineer in the Water Resources Management and Coastal Engineering Laboratory, Technical University of Crete, participating in 10 national and international research projects on water resources, climate change and ecosystem interactions. His main research interests include Water Resources Management, Extreme Weather Events (flooding, drought), Climate Change, and Data Mining from Remote Sensing and Ground-Based Observations.