| How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models? R Hashemi, P Brigode, PA Garambois, P Javelle Hydrology and Earth System Sciences 26, 5793-5816, 2022 | 18 | 2022 |
| A Numerical study on three-dimensionality and turbulence in supercritical bend flow R Hashemi, MM Namin, M Ghaeini-Hessaroeyeh, E Fadaei-Kermani International Journal of Civil Engineering 18 (3), 381-391, 2020 | 9 | 2020 |
| How can regime characteristics of catchments help in training of local and regional LSTM-based runoff models? R Hashemi, P Brigode, PA Garambois, P Javelle Hydrology and Earth System Sciences Discussions 2021, 1-33, 2021 | 5 | 2021 |
| Closing the data gap: runoff prediction in fully ungauged settings using LSTM R Hashemi, P Javelle, O Delestre, S Razavi Hydrology and Earth System Sciences Discussions 2023, 1-41, 2023 | | 2023 |
| Runoff predictive capability of a simple LSTM model versus a proven conceptual model between diverse hydrological regimes R Hashemi, P Brigode, PA Garambois, P Javelle EGU General Assembly Conference Abstracts, EGU21-15103, 2021 | | 2021 |
| Toward real time forecasting rainfall-related incidents on a railway network in France P Javelle, R Hashemi, L Oudin, D Organde, B Salavati, F Chirouze AGU Fall Meeting Abstracts 2019, H11M-1677, 2019 | | 2019 |
