Artificial Intelligence/Machine Learning

that predict floods and droughts, monitor water quality, improve process understanding, and help attributing the extremes to climate change.

Funded projects

EU Horizon 2020

2021-2025 "I-CISK: Innovating climate services through integrating scientific and local knowledge", 5 M Euros, 13 partners (Principal Investigator, PI)

2021-2025 "CLINT: Climate Intelligence: extreme events detection, attribution and adaptation design using machine learning", (nr 101003876), 6 M Euros, 15 partners (PI)

Recommended references

Girons Lopez, M., Crochemore, L., Pechlivanidis, I.G., 2021, 'Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden', Hydrol. Earth Syst. Sci., 25, 1189-1209, https://doi.org/10.5194/hess-25-1189-2021

Macian-Sorribes H., Pechlivanidis I.G., Crochemore L., Pulido-Velazquez M., 2020, 'Fuzzy post-processing to advance the quality of continental seasonal hydrological forecasts for river basin management', Journal of Hydrometeorology, doi: https://doi.org/10.1175/JHM-D-19-0266.1

Pechlivanidis I.G., Crochemore L., Rosberg J., Bosshard T., 2020, 'What are the key drivers controlling the forecasts of seasonal streamflow volumes?', Water Resources Research, doi: 10.1029/2019WR026987

Pechlivanidis I.G., Gupta H., Bosshard T., 2018, 'An information theory approach to identifying a representative subset of hydro-climatic simulations for impact modeling studies', Water Resources Research, doi:10.1029/2017WR022035.