Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Abstract

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.

Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

“…Drought code was the most important variable, followed by anthropogenic features, in both the LR and RF models. These results were consistent with other studies on determinant factors for the occurrence of wildfire where climatic and anthropogenic predictors had a higher influence on the fire occurrence probability [70,[89][90][91][92]. All those models were efficiently applied at a smaller scale (such as national parks or protected areas), while our models showed similar efficacy at a larger scale.…”

A Comparison of Two Machine Learning Classification Methods for Remote Sensing Predictive Modeling of the Forest Fire in the North-Eastern Siberia

“…Banerjee et al claim that the MaxENT method can be used as a decision support tool for stakeholders of forest resources [52]. Pham et al claim that models that consider climate variables, vegetation, and human influences can explain fire risk better than those that only account for some of these factors [53].…”

Improving Voting Feature Intervals for Spatial Prediction of Landslides

“…Performance of the models was evaluated using statistical measures such as positive predictive value (PPV), area under receiver operating characteristic (ROC) curve (AUC), specificity (SPF), accuracy (ACC), negative predictive value (NPV), sensitivity (SST), root mean square error (RSME), and Kappa index (k) [61,62]. Detail description of these indices is presented in relevant studies [4,[63][64][65][66][67][68][69][70]. Formulas of these indices are presented in Table 1.…”