Abstract
Different parameters feed mathematical and/or empirical models. However, the uncertainty (or lack of precision) present in such parameters usually impacts in the quality of the output/recommendation of prediction models. Fortunately, there exist uncertainty reduction methods which enable the obtention of more accurate solutions. One of such methods is ESSIM-DE (Evolutionary Statistical System with Island Model and Differential Evolution), a general purpose method for prediction and uncertainty reduction. ESSIM-DE has been used for the forest fireline prediction, and it is based on statistical analysis, parallel computing, and differential evolution. In this work, we enrich ESSIM-DE with an automatic and dynamic tuning strategy, to adapt the generational parameter of the evolutionary process in order to avoid premature convergence and/or stagnation, and to improve the general performance of the predictive tool. We describe the metrics, the tuning points and actions, and we show the results for different controlled fires.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bianchini, G., et al.: Wildland fire growth prediction method based on multiple overlapping solution. J. Comput. Sci. 1(4), 229–237 (2010)
Caymes-Scutari, P., Bianchini, G., Sikora, A., Margalef, T.: Environment for automatic development and tuning of parallel applications. In: International Conference on High Performance Computing & Simulation (HPCS), Innsbruck, pp. 743–750 (2016)
Healey, J.: The Essentials of Statistics: A Tool for Social Research. Thomson/Wadswort, Belmont (2007). ISBN 9780495009757
Lampinen, J., Zelinka, I.: On stagnation of the differential evolution algorithm, In: Ošmera, P. (ed.) Proceedings of MENDEL 2000, 6th International Mendel Conference on Soft Computing, pp. 76–83. Brno, Czech Republic (2000). http://www.lut.fi/jlampine/MEND2000.ps
Mattson, T., et al.: Patterns for Parallel Programming. Addison-Wesley, Boston (2004)
Naono, K., Teranishi, K., Cavazos, J., Suda, R. (eds.): Software Automatic Tuning: From Concepts to State-of-the-Art Results. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-6935-4
Real, R., Vargas, J.M.: The probabilistic basis of Jacard’s index of similarity. Syst. Biol. 45(3), 380–385 (1996)
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. NCS. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-31306-0
Talbi, E.: Metaheuristics: From Design to Implementation. Wiley, Reading (2009)
Tardivo, M.L., Caymes-Scutari, P., Méndez-Garabetti, M., Bianchini, G.: Optimization for an uncertainty reduction method applied to forest fires spread prediction. In: De Giusti, A.E. (ed.) CACIC 2017. CCIS, vol. 790, pp. 13–23. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75214-3_2
Tardivo, L., Caymes-Scutari, P., Bianchini, G., Mendez-Garabetti, M.: Sintonización Dinámica del Método Paralelo de Predicción de Incendios Forestales ESSIM-DE. In: Proceedings XXV Congreso Argentino de Ciencias de la Computación (CACIC 2019), pp. 115–124. UniRio Editora, Río Cuarto (2019). ISBN 978-987-688-377-1
Viegas, D.X.: Project Spread Forest Fire Spread Prevention and Mitigation (2004). https://cordis.europa.eu/project/rcn/60354/factsheet/fr. Accessed Sept 2019
Yang, M., Li, C., Cai, Z., Guan, J.: Differential evolution with auto-enhanced population diversity. IEEE Trans. Cybern. 45, 302–315 (2015)
El País. https://elpais.com/elpais/2019/09/11/album/1568221457_486259.html#foto_gal_1
Radio Gráfica. https://radiografica.org.ar/2020/01/08/australia-la-ciencia-predijo-los-incendios-y-la-politica-lo-ignoro/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Caymes-Scutari, P., Tardivo, M.L., Bianchini, G., Méndez-Garabetti, M. (2020). Dynamic Tuning of a Forest Fire Prediction Parallel Method. In: Pesado, P., Arroyo, M. (eds) Computer Science – CACIC 2019. CACIC 2019. Communications in Computer and Information Science, vol 1184. Springer, Cham. https://doi.org/10.1007/978-3-030-48325-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-48325-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-48324-1
Online ISBN: 978-3-030-48325-8
eBook Packages: Computer ScienceComputer Science (R0)