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HESS-IM: A Uncertainty Reduction Method that Integrates Remote Sensing Data Applied to Forest Fire Behavior Prediction

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Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1444))

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Abstract

Natural disasters alter the stability and living conditions in the environment. Natural disasters are caused by different types of hazards, which can be of natural or man-made origin, the latter in turn can be intentional or unintentional. Forest fires cause great losses and harms every year, some of which are often irreparable. Among the different strategies and technologies available to mitigate the effects of fire, the forest fire behavior prediction may be a promising strategy. This approach allows for identifying areas at greatest risk of being burned, thereby permitting to take decisions in advance to reduce losses and damages. In this work, we present a Hybrid Evolutionary-Statistical System with Island Model (HESS-IM), a new approach of the uncertainty reduction method that integrates remote sensing data applied to forest fire behavior prediction. HESS-IM uses hybrid metaheuristics under a collaborative approach as an optimization technique, satellite images for application to real cases, statistical analysis, and heterogeneous high-performance computing to generate predictions in the shortest time possible.

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Author information

Authors and Affiliations

  1. Laboratorio de Investigación en Cómputo Paralelo/Distribuido (LICPaD), Departamento de Ingeniería en Sistemas de Información Facultad Regional Mendoza, Universidad Tecnológica Nacional, Mendoza, Argentina

    Miguel Méndez-Garabetti, Germán Bianchini & Paola Caymes-Scutari

  2. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina

    Miguel Méndez-Garabetti & Paola Caymes-Scutari

Authors
  1. Miguel Méndez-Garabetti
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  2. Germán Bianchini
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  3. Paola Caymes-Scutari
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Corresponding author

Correspondence to Miguel Méndez-Garabetti .

Editor information

Editors and Affiliations

  1. III-LIDI, Facultad de Informática, Universidad Nacional de La Plata, La Plata, Argentina

    Marcelo Naiouf

  2. III-LIDI, Facultad de Informática, Universidad Nacional de La Plata and CIC, La Plata, Argentina

    Enzo Rucci

  3. III-LIDI, Facultad de Informática, Universidad Nacional de La Plata, La Plata, Argentina

    Franco Chichizola

  4. III-LIDI, Facultad de Informática, Universidad Nacional de La Plata and CIC, La Plata, Argentina

    Laura De Giusti

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Méndez-Garabetti, M., Bianchini, G., Caymes-Scutari, P. (2021). HESS-IM: A Uncertainty Reduction Method that Integrates Remote Sensing Data Applied to Forest Fire Behavior Prediction. In: Naiouf, M., Rucci, E., Chichizola, F., De Giusti, L. (eds) Cloud Computing, Big Data & Emerging Topics. JCC-BD&ET 2021. Communications in Computer and Information Science, vol 1444. Springer, Cham. https://doi.org/10.1007/978-3-030-84825-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-84825-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84824-8

  • Online ISBN: 978-3-030-84825-5

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