Wildfire Prediction Model Based on Spatial and Temporal Characteristics: A Case Study of a Wildfire in Portugal’s Montesinho Natural Park

Abstract

Wildfires influence the global carbon cycle, and the regularity of wildfires is mostly determined by elements such as meteorological conditions, combustible material states, and human activities. The time series and spatial dispersion of wildfires have been studied by some scholars. Wildfire samples were acquired in a monthly series for the Montesinho Natural Park historical fire site dataset (January 2000 to December 2003), which can be used to assess the possible effects of geographical and temporal variations on forest fires. Based on the above dataset, dynamic wildfire distribution thresholds were examined using a K-means++ clustering technique for each subgroup, and monthly series data were categorized as flammable or non-flammable depending on the thresholds. A five-fold hierarchical cross-validation strategy was used to train four machine learning models: extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and decision tree (DT). Finally, to explore the performance of those we have mentioned, we used accuracy (ACC), F1 score (F1), and the values for the area under the curve (AUC) of the receiver operating characteristics (ROCs). The results depicted that the XGBoost model works best under the evaluation of the three metrics (ACC = 0.8132, F1 = 0.7862, and AUC = 0.8052). The model performance is significantly improved when compared to the approach of classifying wildfires by burned area size (ACC = 72.3%), demonstrating that spatiotemporal heterogeneity has a broad influence on wildfire occurrence. The law of a spatiotemporal distribution connection in wildfires could aid in the prediction and management of wildfires and fire disasters.

Forest fire vulnerability in Nepal's Chure region: Investigating the influencing factors using generalized linear model

“…The AUC value received by our model is higher than Parajuli et al [ 29 ] by fuzzy AHP (AUC = 0.83) and similar to that of Goleiji et al [ 113 ] using AHP and analytical network process (AUC = 0.92). Furthermore, our models outscored the AUC and ACC values by various machine learning methods by previous research [ 114 , 115 ]. The final susceptible map produced through the best fit GLM model shows that the western and central parts of Nepal as the high-risk areas for fire occurrence, suggesting the concerned authorities to take proper management measures to avoid the potential loss.…”

Modeling Fire Boundary Formation Based on Machine Learning in Liangshan, China

“…Therefore, we constructed the matched case-control conditional light gradient boosting machine (MCC CLightGBM) to predict the probability of fire boundary formation at different distances from the fire environment and to give the importance of the factors influencing fire boundary formation in the study area by choosing the optimal model. We evaluated the area under the curve (AUC), F1-score, and accuracy (ACC) of each model [20,21]. These are useful indicators of the closeness of the predicted and actual fire occurrence in the study area (burned area, unburned area, and boundaries).…”

A Harmful Algal Bloom Detection Model Combining Moderate Resolution Imaging Spectroradiometer Multi-Factor and Meteorological Heterogeneous Data

“…However, few studies have integrated satellite data and heterogeneous meteorological information for the construction of HAB detection models. With the rise of machine learning algorithms bringing new research prospects for processing remote sensing data [28][29][30], as well as the advent of MODIS-derived ocean color products, which have been widely used in the detection of marine disaster events [31,32], early researchers utilized oceancolor satellite components such as SeaWiFS and MODIS to distinguish phytoplankton (including harmful algal blooms) by inverting chlorophyll concentrations [33]. In addition, chlorophyll-a was identified as one of the important factors for assessing harmful algal blooms [34].…”