Evaluating the Impact of Recursive Feature Elimination on Machine Learning Models for Predicting Forest Fire-Prone Zones

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F24%3A10496890" target="_blank" >RIV/00216208:11310/24:10496890 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KC_xAV1ueg" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KC_xAV1ueg</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/fire7120440" target="_blank" >10.3390/fire7120440</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluating the Impact of Recursive Feature Elimination on Machine Learning Models for Predicting Forest Fire-Prone Zones

Popis výsledku v původním jazyce

This study aimed to enhance the accuracy of forest fire susceptibility mapping (FSM) by innovatively applying recursive feature elimination (RFE) with an ensemble of machine learning models, specifically Support Vector Machine (SVM) and Random Forest (RF), to identify key fire factors. The fire zones were derived from MODIS satellite imagery from 2012 to 2017. Further validation of these data has been provided by field surveys and reviews of land records in rangelands and forests; a total of 326 fire points were determined in this study. Seventeen factors involving topography, geomorphology, meteorology, hydrology, and human factors were identified as being effective primary factors in triggering and spreading fires in the selected mountainous case study area. As a first step, the RFE models RF, Extra Trees, Gradient Boosting, and AdaBoost were used to identify important fire factors among all selected primary factors. The SVM and RF models were applied once on all factors and secondly on those derived from the RFE model as the key factors in FSM. Training and testing data were divided tenfold, and the model&apos;s performance was evaluated using cross-validation. Various metrics, including recall, precision, F1 score, accuracy, area under the curve (AUC), Matthew&apos;s correlation coefficient (MCC), and Kappa, were employed to measure the performance of the models. The assessments demonstrate that leveraging RFE models enhances the FSM results by identifying key factors and excluding unnecessary ones. Notably, the SVM model exhibits significant improvement, achieving an increase of over 10.97% in accuracy and 8.61% in AUC metrics. This improvement underscores the effectiveness of the RFE approach in enhancing the predictive performance of the SVM model.

Název v anglickém jazyce

Evaluating the Impact of Recursive Feature Elimination on Machine Learning Models for Predicting Forest Fire-Prone Zones

Popis výsledku anglicky

This study aimed to enhance the accuracy of forest fire susceptibility mapping (FSM) by innovatively applying recursive feature elimination (RFE) with an ensemble of machine learning models, specifically Support Vector Machine (SVM) and Random Forest (RF), to identify key fire factors. The fire zones were derived from MODIS satellite imagery from 2012 to 2017. Further validation of these data has been provided by field surveys and reviews of land records in rangelands and forests; a total of 326 fire points were determined in this study. Seventeen factors involving topography, geomorphology, meteorology, hydrology, and human factors were identified as being effective primary factors in triggering and spreading fires in the selected mountainous case study area. As a first step, the RFE models RF, Extra Trees, Gradient Boosting, and AdaBoost were used to identify important fire factors among all selected primary factors. The SVM and RF models were applied once on all factors and secondly on those derived from the RFE model as the key factors in FSM. Training and testing data were divided tenfold, and the model&apos;s performance was evaluated using cross-validation. Various metrics, including recall, precision, F1 score, accuracy, area under the curve (AUC), Matthew&apos;s correlation coefficient (MCC), and Kappa, were employed to measure the performance of the models. The assessments demonstrate that leveraging RFE models enhances the FSM results by identifying key factors and excluding unnecessary ones. Notably, the SVM model exhibits significant improvement, achieving an increase of over 10.97% in accuracy and 8.61% in AUC metrics. This improvement underscores the effectiveness of the RFE approach in enhancing the predictive performance of the SVM model.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Fire

ISSN

2571-6255

e-ISSN

2571-6255

Svazek periodika

7

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

24

Strana od-do

440

Kód UT WoS článku

001384375000001

EID výsledku v databázi Scopus

2-s2.0-85213242976