Forest Fire Prediction with Imbalanced Data Using a Deep Neural Network Method

Abstract

Forests suffer from heavy losses due to the occurrence of fires. A prediction model based on environmental condition, such as meteorological and vegetation indexes, is considered a promising tool to control forest fires. The construction of prediction models can be challenging due to (i) the requirement of selection of features most relevant to the prediction task, and (ii) heavily imbalanced data distribution where the number of large-scale forest fires is much less than that of small-scale ones. In this paper, we propose a forest fire prediction method that employs a sparse autoencoder-based deep neural network and a novel data balancing procedure. The method was tested on a forest fire dataset collected from the Montesinho Natural Park of Portugal. Compared to the best prediction results of other state-of-the-art methods, the proposed method could predict large-scale forest fires more accurately, and reduces the mean absolute error by 3–19.3 and root mean squared error by 0.95–19.3. The proposed method can better benefit the management of wildland fires in advance and the prevention of serious fire accidents. It is expected that the prediction performance could be further improved if additional information and more data are available.

Predicting wildfires in Algerian forests using machine learning models

“…Limitations of wildfires prediction methods : We give here, some of the challenges or limitations of forest fire predictors, encountered in the literature [ 5 , 6 ]. Data availability and quality: Forest fire prediction requires a large amount of data from various sources such as weather, vegetation, topography, human activities, etc.…”

Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas

“…For example, one wildfire prediction study used multi-year, fire incident data that were collected in the Montesinho Natural Park of Portugal. This study showed the success of ANNs locating potential sites of large-scale wildfire outbreaks but struggling with smaller-scale fire incidents [25]. Another study completed in Heilongjiang, Northeast China used ANNs comparatively with logistic regression models to determine the most effective algorithm for wildfire outbreak prediction.…”

Prediction of Peatlands Forest Fires in Malaysia Using Machine Learning

“…By leveraging the inherent capacity of neural networks to learn complex patterns from data [13], our approach aims to enhance the predictive capabilities for forest fire occurrence in peatland areas. The justification for employing machine learning techniques in this context is supported by prior studies [14,15] demonstrating their efficacy in capturing intricate relationships and achieving improved predictive performance in various domains.…”