Uncertainty study on atmospheric dispersion simulations using meteorological ensembles with a Monte Carlo approach, applied to the Fukushima nuclear accident

Autor: Ngoc Bao Tran LE, Irène Korsakissok, Vivien Mallet, Raphaël Périllat, Anne Mathieu

Publikováno v: Atmospheric Environment: X, Vol 10, Iss , Pp 100112- (2021)

In emergency cases, when nuclear accidental releases take place, numerical models, developed by French Institute of Radiation Protection and Nuclear Safety (IRSN), are used to forecast the atmospheric dispersion of radionuclides. These models compute

Externí odkaz: https://doaj.org/article/9c3928b728d044529f86cd0323b55cd9

Simulation-based high resolution fire danger mapping using deep learning

Autor: Frédéric Allaire, Jean-Baptiste Filippi, Vivien Mallet, Florence Vaysse

Publikováno v: International Journal of Wildland Fire
International Journal of Wildland Fire, 2022, 31 (4), pp.379-394. ⟨10.1071/WF21143⟩

Wildfire occurrence and behaviour are difficult to predict locally for the next day. In the present work, we propose relying on fire spread simulations to provide a fire danger index representative of the potential for fire spread that includes not o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c83f810f70516d65fe124cb0b11f5ff
https://inria.hal.science/hal-03189847/file/paper.pdf

Emulation of wildland fire spread simulation using deep learning

Autor: Frederic Allaire, Vivien Mallet, Jean Baptiste Filippi

Publikováno v: Neural Networks
Neural Networks, Elsevier, 2021, 141, pp.184-198. ⟨10.1016/j.neunet.2021.04.006⟩

Numerical simulation of wildland fire spread is useful to predict the locations that are likely to burn and to support decision in an operational context, notably for crisis situations and long-term planning. For short-term, the computational time of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee5f575520be11578c1492aa0c2bf923
https://hal.inria.fr/hal-03142281/file/paper.pdf

Ensemble forecast of photovoltaic power with online CRPS learning

Autor: Jean Thorey, Vivien Mallet, C. Chaussin

Publikováno v: International Journal of Forecasting
International Journal of Forecasting, Elsevier, 2018, 34 (4), pp.762-773. ⟨10.1016/j.ijforecast.2018.05.007⟩
International Journal of Forecasting, 2018, 34 (4), pp.762-773. ⟨10.1016/j.ijforecast.2018.05.007⟩

International audience; We provide probabilistic forecasts of photovoltaic (PV) production, for several PV plants located in France up to 6 days of lead time, with a 30-min timestep. First, we derive multiple forecasts from numerical weather predicti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81d494e160ba01f06291af26dda2fcf5
https://doi.org/10.1016/j.ijforecast.2018.05.007

Mobile Crowd-Sensing as a Resource for Contextualized Urban Public Policies: A Study using Three Use Cases on Noise and Soundscape Monitoring

Autor: Valérie Issarny, Bruno Lefevre, Rachit Agarwal, Vivien Mallet

Publikováno v: Cities & Health
Cities & Health, Taylor & Francis, 2019, ⟨10.1080/23748834.2019.1617656⟩
Cities & Health, 2019, ⟨10.1080/23748834.2019.1617656⟩

International audience; Environmental noise is a major pollutant in contemporary cities and calls for the ac-10 tive monitoring of noise levels to spot the locations where it most affects the people's health and well-being. However, due to the comple

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19c8127dc599a88e74667069a3154312
https://hal.inria.fr/hal-02127052v2/document