Abstract
In this study, a new organization based system for forest fires prediction is presented. It is an Organization Based System for Forest Fires Forecasting (OBSFFF). The core of the system is based on the Case-Based Reasoning methodology, and it is able to generate a prediction about the evolution of the forest fires in certain areas. CBR uses historical data to create new solutions to current problems. The system employs a distributed multi-agent architecture so that the main components of the system can be remotely accessed. All the elements building the final system, communicate in a distributed way, from different type of interfaces and devices. OBSFFF has been applied to generate predictions in real forest fire situations, using historical data both to train the system and to check the results. Results have demonstrated that the system accurately predicts the evolution of the fires. It has been demonstrated that using a distributed architecture enhances the overall performance of the system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Long, D.G.: Mapping fire regimes across time and space: Understanding coarse and fine-scale fire patterns. International Journal of Wildland Fire 10, 329–342 (2001)
Mazzeo, G., Marchese, F., Filizzola, C., Pergola, N., et al.: A Multi-temporal Robust Satellite Technique (RST) for Forest Fire Detection. Analysis of Multi-temporal Remote Sensing Images, 1–6 (2007)
Iliadis, L.S.: A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation. Environmental Modelling and Software 20(5), 613–621 (2005)
Serón, F.J., Gutiérrez, D., Magallón, J., Ferragut, L., et al.: The Evolution of a Wildland Forest Fire Front. The Visual Computer 21(3), 152–169 (2005)
GESTOSA, ADAI-CEIF(Center of Forest Fire Studies) (2005), http://www.adai.pt/ceif/Gestosa/
Gasser, L.: Perspectives on organizations in multi-agent systems. In: Luck, M., Mařík, V., Štěpánková, O., Trappl, R. (eds.) ACAI 2001 and EASSS 2001. LNCS (LNAI), vol. 2086, pp. 1–16. Springer, Heidelberg (2001)
Ferber, J., Gutknecht, O., Michel, F.: From agents to organizations: an organizational view of multi-agent systems. LNCS, pp. 214–230. Springer, Heidelberg (2004)
Dignum, V., Dignum, F.: A logic for agent organizations. FAMAS@ Agents, 3–7 (2007)
Karayiannis, N.B., Mi, G.W.: Growing radial basis neural networks: merging supervised andunsupervised learning with network growth techniques. IEEE Transactions on Neural Networks 8(6), 1492–1506 (1997)
Mata, A., Corchado, J.M.: Forecasting the probability of finding oil slicks using a CBR system. Expert Systems With Applications 36(4), 8239–8246 (2009)
Cerami, E.: Web Services Essentials Distributed Applications with XML-RPC, SOAP, UDDI & WSDL. O’Reilly & Associates, Inc., Sebastopol (2002)
Gunter, S., Schraudolph, N.N., Vishwanathan, S.V.N.: Fast Iterative Kernel Principal Component Analysis. Journal of Machine Learning Research 8, 1893–1918 (2007)
Fritzke, B.: Growing cell structures—a self-organizing network for unsupervised and supervised learning. Neural Networks 7(9), 1441–1460 (1994)
Ros, F., Pintore, M., Chrétien, J.R.: Automatic design of growing radial basis function neural networks based on neighboorhood concepts. Chemometrics and Intelligent Laboratory Systems 87(2), 231–240 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mata, A., Pérez, B., Corchado, J.M. (2010). Forest Fires Prediction by an Organization Based System. In: Demazeau, Y., Dignum, F., Corchado, J.M., Pérez, J.B. (eds) Advances in Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12384-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-12384-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12383-2
Online ISBN: 978-3-642-12384-9
eBook Packages: EngineeringEngineering (R0)