Enhanced forest fire susceptibility mapping by integrating feature selection genetic algorithm and bagging-based support vector machine with artificial neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256529" target="_blank" >RIV/61989100:27240/24:10256529 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s00477-024-02851-8" target="_blank" >https://link.springer.com/article/10.1007/s00477-024-02851-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00477-024-02851-8" target="_blank" >10.1007/s00477-024-02851-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhanced forest fire susceptibility mapping by integrating feature selection genetic algorithm and bagging-based support vector machine with artificial neural networks

Popis výsledku v původním jazyce

Forest fire is a natural disaster that threatens a large part of the world&apos;s forests. Considering the destructive effects of forest fires, the preparation of forest fire probability maps can be a very valuable step towards reducing such effects. This study proposes two novel wrapper feature selection-based ensemble models that combine the strengths of Support vector machine (SVM) and Artificial neural networks (ANN) with bagging (bootstrap aggregating) and Genetic Algorithm (GA) for forest fire susceptibility mapping in the Jerash and Ajloun provinces of Jordan. By integrating multiple learning algorithms through ensemble methods, we aim to increase predictive accuracy and enhance the robustness of our findings. GA was employed for feature selection utilizing data from 207 forest fire locations and fourteen predictor variables. 70% of the forest fire locations (145 locations) were used in the training phase, and the remaining 60% (62 locations) were employed to validate the models. The accuracy of the models was measured by using the area Under the Receiver Operating Characteristic (AUROC). The AUROC for single SVM, single ANN, GBSVM, and GBANN models was 69.3%, 66.9%, 70.9%, and 70.4% in the validation phase, respectively. The results showed that wrapper and bagging-based ensemble models did much better than single models. This shows that combining techniques can improve modeling performance for mapping the risk of forest fires. (C) The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Název v anglickém jazyce

Enhanced forest fire susceptibility mapping by integrating feature selection genetic algorithm and bagging-based support vector machine with artificial neural networks

Popis výsledku anglicky

Forest fire is a natural disaster that threatens a large part of the world&apos;s forests. Considering the destructive effects of forest fires, the preparation of forest fire probability maps can be a very valuable step towards reducing such effects. This study proposes two novel wrapper feature selection-based ensemble models that combine the strengths of Support vector machine (SVM) and Artificial neural networks (ANN) with bagging (bootstrap aggregating) and Genetic Algorithm (GA) for forest fire susceptibility mapping in the Jerash and Ajloun provinces of Jordan. By integrating multiple learning algorithms through ensemble methods, we aim to increase predictive accuracy and enhance the robustness of our findings. GA was employed for feature selection utilizing data from 207 forest fire locations and fourteen predictor variables. 70% of the forest fire locations (145 locations) were used in the training phase, and the remaining 60% (62 locations) were employed to validate the models. The accuracy of the models was measured by using the area Under the Receiver Operating Characteristic (AUROC). The AUROC for single SVM, single ANN, GBSVM, and GBANN models was 69.3%, 66.9%, 70.9%, and 70.4% in the validation phase, respectively. The results showed that wrapper and bagging-based ensemble models did much better than single models. This shows that combining techniques can improve modeling performance for mapping the risk of forest fires. (C) The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Stochastic Environmental Research and Risk Assessment

ISSN

1436-3240

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

38

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

5039-5058

Kód UT WoS článku

001353858900001

EID výsledku v databázi Scopus

2-s2.0-85208980291