Wind Field Parallelization Based on Python Multiprocessing to Reduce Forest Fire Propagation Prediction Uncertainty - researchr publication related

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Gemma Sanjuan, Tomàs Margalef, Ana Cortés. Wind Field Parallelization Based on Python Multiprocessing to Reduce Forest Fire Propagation Prediction Uncertainty. In Valeria V. Krzhizhanovskaya, Gábor Závodszky, Michael Harold Lees, Jack J. Dongarra, Peter M. A. Sloot, Sérgio Brissos, João Teixeira, editors, Computational Science - ICCS 2020 - 20th International Conference, Amsterdam, The Netherlands, June 3-5, 2020, Proceedings, Part VII. Volume 12143 of Lecture Notes in Computer Science, pages 550-560, Springer, 2020. [doi]

The following publications are possibly variants of this publication:

Wind Field Uncertainty in Forest Fire Propagation PredictionGemma Sanjuan, Carlos Brun, Tomàs Margalef, Ana Cortés. iccs 2014: 1535-1545 [doi]

Determining map partitioning to minimize wind field uncertainty in forest fire propagation predictionGemma Sanjuan, Carlos Brun, Tomàs Margalef, Ana Cortés. jocs, 14:28-37, 2016. [doi]

Effect of Wind Field Parallelization on Forest Fire Spread PredictionGemma Sanjuan, Carlos Brun, Ana Cortés, Tomàs Margalef. iccsa 2014: 538-549 [doi]

Coupling Wind Dynamics into a DDDAS Forest Fire Propagation Prediction SystemCarlos Brun, Tomàs Artés, Tomàs Margalef, Ana Cortés. iccs 2012: 1110-1118 [doi]

Forest Fire Propagation Prediction Based on Overlapping DDDAS ForecastsTomàs Artés, Adrián Cardil, Ana Cortés, Tomàs Margalef, Domingo Molina, Lucas Pelegrín, Joaquín Ramírez. iccs 2015: 1623-1632 [doi]

A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation predictionMaría Laura Tardivo, Paola Caymes-Scutari, Germán Bianchini, Miguel Méndez-Garabetti, Andrés Cencerrado, Ana Cortés. iccs 2017: 2018-2027 [doi]

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