Assessment of Three Machine Learning Techniques with Open-Access Geographic Data for Forest Fire Susceptibility Monitoring—Evidence from Southern Ecuador

Forest fires have become a habitual threat in all types of ecosystems, which is the reason why it is necessary to improve management of the territories and optimization of prevention and means of extinction. This study compares three machine learning techniques: logistic regression, logistic decisio...

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Bibliographic Details
Published in:Forests 2022-03, Vol.13 (3), p.474
Main Authors: Reyes-Bueno, Fabián, Loján-Córdova, Julia
Format: Article
Language:English
Subjects:
Decision making
Decision trees
Elevation
Forest & brush fires
Forest fires
Humidity
Learning algorithms
logistic decision trees
logistic regression
Machine learning
Meteorological data
Moisture effects
Optimization
Rain
Seasons
spatial modeling
Strategic management
susceptibility
Topography
Variables
Vegetation
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Summary:Forest fires have become a habitual threat in all types of ecosystems, which is the reason why it is necessary to improve management of the territories and optimization of prevention and means of extinction. This study compares three machine learning techniques: logistic regression, logistic decision tree, and multivariate adaptive regression spline to identify areas susceptible to forest fires in the Loja canton. In the training of the machine learning models, a multitemporal database with 1436 points was used, fed with the information from seven variables related to fuel moisture, proximity to anthropic activities, and ground elevation. After analyzing the performance of the three models, better results were observed with the LMT, thus offering application ease for local decision-makers. The results show that the technique used allowed generating a model with a good predictive capacity and that the maps resulting from the model can be updated in short periods of time. However, it is necessary to highlight the lack meteorological data availability at the local level and to encourage future researchers to implement improvements in this regard.
ISSN:1999-4907
1999-4907
DOI:10.3390/f13030474