Abstract
Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Honolulu, Hawaii, by NewsRx correspondents, research stated, “This study presents a comparative analysis of four Machine Learning (ML) models used to map wildfire susceptibility on HawaiModified Letter Turned Commai Island, HawaiModified Letter Turned Commai. Extreme Gradient Boosting (XGBoost) combined with three meta-heuristic algorithms - Whale Optimization (WOA), Black Widow Optimization (BWO), and Butterfly Optimization (BOA) - were employed to map areas susceptible to wildfire.” Financial support for this research came from Hawaii-Emergency Management Agency (HI-EMA) grant. Our news editors obtained a quote from the research from the University of Hawaii Manoa, “To generate a wildfire inventory, 1408 wildfire points were identified within the study area from 2004 to 2022. The four ML models (XGBoost, WOA-XGBoost, BWOXGBoost, and BOA-XGBoost) were run using 14 wildfire-conditioning factors categorized into four main groups: topographical, meteorological, vegetation, and anthropogenic. Six performance metrics - sensitivity, specificity, positive predictive values, negative predictive values, the Area Under the receiver operating characteristic Curve (AUC), and the average precision (AP) of Precision-Recall Curves (PRCs) - were used to compare the predictive performance of the ML models. The SHapley Additive exPlanations (SHAP) framework was also used to interpret the importance values of the 14 influential variables for the modeling of wildfire on HawaiModified Letter Turned Commai Island using the four models. The results of the wildfire modeling indicated that all four models performed well, with the BWOXGBoost model exhibiting a slightly higher prediction performance (AUC = 0.9269), followed by WOAXGBoost (AUC = 0.9253), BOA-XGBoost (AUC = 0.9232), and XGBoost (AUC = 0.9164). SHAP analysis revealed that the distance from a road, annual temperature, and elevation were the most influential factors.”
NETL
NSTL
Elsevier