Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

Abstract

Wildfires are one of the most common natural hazards worldwide. Here, we compared the capability of bivariate and multivariate models for the prediction of spatially explicit wildfire probability across a fire-prone landscape in the Zagros ecoregion, Iran. Dempster–Shafer-based evidential belief function (EBF) and the multivariate logistic regression (LR) were applied to a spatial dataset that represents 132 fire events from the period of 2007–2014 and twelve explanatory variables (altitude, aspect, slope degree, topographic wetness index (TWI), annual temperature, and rainfall, wind effect, land use, normalized difference vegetation index (NDVI), and distance to roads, rivers, and residential areas). While the EBF model successfully characterized each variable class by four probability mass functions in terms of wildfire probabilities, the LR model identified the variables that have a major impact on the probability of fire occurrence. Two distribution maps of wildfire probability were developed based upon the results of each model. In an ensemble modeling perspective, we combined the two probability maps. The results were verified and compared by the receiver operating characteristic (ROC) and the Wilcoxon Signed-Rank Test. The results showed that although an improved predictive accuracy (AUC = 0.864) can be achieved via an ensemble modeling of bivariate and multivariate statistics, the models fail to individually provide a satisfactory prediction of wildfire probability (EBFAUC = 0.701; LRAUC = 0.728). From these results, we recommend the employment of ensemble modeling approaches for different wildfire-prone landscapes.

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

“…As such, machine learning modeling can effectively alleviate the difficulty associated with the identification of groundwater potential zones over large-scale regions, which often suffer a lack of accurate and long-term geotechnical and hydrogeological data for the implementation of physically based and/or numerical models [11]. However, the utility of different machine learning methods should be broadly investigated via their applications in different regions with different geo-environmental settings to find the best model with the highest accuracy and lowest sensitivity to noisy input data [33,70,87,90,91].…”

“…LR is the most widely used empirical model in different fields of science, in particular for environmental studies [30][31][32][33]. In LR, the probability of occurrence of a phenomenon is estimated within the range of 0 to 1, and it is not necessary to assume the normality of the predictor variables.…”

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

“…Curvature of the ground is important as concave surface are more suitable for holding the surface water thus helps in recharging the area. Aspect give direction of slope and thus provide information of incidence of rainfall [20][21][22][23]. Slope provide important information of runoff and accumulation of water thus of recharge.…”

Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

“…(iii) Data preparation: in this study, the holdout validation method was used for training and validating the models as it is a popular and effective method for generating the datasets for training and testing the models [24,[44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61], and thus the collected data were divided into two parts. The first part included 70% data which was used to train the models, whereas the second part contained 30%, the remaining data and this was used to validate the models as the ratio 70/30 for dividing the training and testing dataset was a common ratio used in applying the ML models [29,[62][63][64][65][66][67][68][69][70][71]. (iv) Training the models: the models were created using a 70% training dataset.…”