A data fusion framework with novel hybrid algorithm for multi-agent Decision Support System for Forest Fire

Authors:
Çetin Elmas;Yusuf Sönmez
Affiliations:
Department of Electrical Education, Technical Education Faculty, Gazi University, Ankara 06500, Turkey;Department of Electrical Technology, Gazi Vocational College, Gazi University, Ankara 06760, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In this study Forest Fire Decision Support System (FOFDESS) which is a multi-agent Decision Support System for Forest Fire has been presented. Depending on the existing meteorological state and environmental observations, FOFDESS does the fire danger rating by predicting the forest fire and it can also approximate fire spread speed and quickly detect a started fire. Some data fusion algorithms such as Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), Fuzzy Switching (FS) and image processing have been used for these operations in FOFDESS. These algorithms have been brought together by a designed data fusion framework and a novel hybrid algorithm called NABNEF (Naive Bayes Aided Neural-Fuzzy Algorithm) has been improved for fire danger rating in FOFDESS. In this state, FOFDESS is an integrated system which includes the dimensions of prediction, detection and management. As a result of the experiments, it was found out that FOFDESS helped determining the most accurate strategy for fire fighting by producing effective results.