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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

Stochastic Environmental Research and Risk Assessment

ISSN

1436-3240

e-ISSN

—

Volume of the periodical

12

Issue of the periodical within the volume

38

Country of publishing house

US - UNITED STATES

Number of pages

19

Pages from-to

5039-5058

UT code for WoS article

001353858900001

EID of the result in the Scopus database

2-s2.0-85208980291