Journal ArticleDOI
An intelligent system for forest fire risk prediction and fire fighting management in Galicia
Amparo Alonso-Betanzos,Oscar Fontenla-Romero,Bertha Guijarro-Berdiñas,Elena Hernández-Pereira,Marı́a Inmaculada Paz Andrade,Eulogio Jiménez,José Luis Legido Soto,Tarsy Carballas +7 more
TLDR
A system developed for the region of Galicia in NW Spain, one of the regions of Europe most affected by fires, that acts as a preventive tool by predicting forest fire risks, backs up the forest fire monitoring and extinction phase, and assists in planning the recuperation of the burned areas.Abstract:
Over the last two decades in southern Europe, more than 10 million hectares of forest have been damaged by fire. Due to the costs and complications of fire-fighting a number of technical developments in the field have been appeared in recent years. This paper describes a system developed for the region of Galicia in NW Spain, one of the regions of Europe most affected by fires. This system fulfills three main aims: it acts as a preventive tool by predicting forest fire risks, it backs up the forest fire monitoring and extinction phase, and it assists in planning the recuperation of the burned areas. The forest fire prediction model is based on a neural network whose output is classified into four symbolic risk categories, obtaining an accuracy of 0.789. The other two main tasks are carried out by a knowledge-based system developed following the CommonKADS methodology. Currently we are working on the trail of the system in a controlled real environment. This will provide results on real behaviour that can be used to fine-tune the system to the point where it is considered suitable for installation in a real application environment.read more
Citations
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Journal ArticleDOI
A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques
TL;DR: Technologies related to UAV forest fire monitoring, detection, and fighting are briefly reviewed, including those associated with fire detection, diagnosis, and prognosis, image vibration elimination, and cooperative control of UAVs.
Journal ArticleDOI
A review of machine learning applications in wildfire science and management
Piyush Jain,Piyush Jain,Sean C. P. Coogan,Sriram Subramanian,Mark Crowley,Steve W. Taylor,Mike D. Flannigan +6 more
TL;DR: Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems as discussed by the authors, and it has rapidly accelerated the field's development.
Journal ArticleDOI
A review of machine learning applications in wildfire science and management
Piyush Jain,Piyush Jain,Sean C. P. Coogan,Sriram Subramanian,Mark Crowley,Steve W. Taylor,Mike D. Flannigan +6 more
TL;DR: A scoping review of ML in wildfire science and management, identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms.
Journal ArticleDOI
Mobile computing in urban emergency situations: Improving the support to firefighters in the field
Alvaro Monares,Sergio F. Ochoa,José A. Pino,Valeria Herskovic,Juan Rodríguez-Covili,Andrés Neyem +5 more
TL;DR: A low-cost mobile collaborative application which may be used in emergency situations to overcome most of the firefighters' communication problems and allows ad hoc communication, decisions support and collaboration among firefighters in the field using mobile devices.
Journal ArticleDOI
Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem
TL;DR: In this paper, an artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey using a multiple data assessment technique.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
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Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
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Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
Book
Knowledge Engineering and Management: The CommonKADS Methodology
Guus Schreiber,Hans Akkermans +1 more
TL;DR: The CommonKADS methodology, developed over the last decade by an industry-university consortium led by the authors, is used and makes as much use as possible of the new UML notation standard.