Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

Forest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help re...

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Bibliographic Details
Published in:Advances in Civil Engineering Vol. 2022; no. 1
Main Authors: Abujayyab, Sohaib K. M., Kassem, Moustafa Moufid, Khan, Ashfak Ahmad, Wazirali, Raniyah, Coşkun, Mücahit, Taşoğlu, Enes, Öztürk, Ahmet, Toprak, Ferhat
Format: Journal Article
Language:English
Published: New York Hindawi 2022
John Wiley & Sons, Inc
Wiley
Subjects:
Accuracy
Algorithms
Case studies
China
Civil engineering
Data mining
Decision trees
Disaster management
Emergency preparedness
Floods
Forest & brush fires
Forest fires
Geographic information systems
Human factors
Humidity
Local government
Machine learning
Mapping
Mediterranean region
Natural disasters
Performance evaluation
Regression analysis
Risk assessment
Support vector machines
Wildfires
Mediterranean region
China
Yunnan China
Turkey
Mediterranean Area
ISSN:1687-8086, 1687-8094
Online Access:Get full text
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Summary:Forest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help reduce the occurrence of such calamities. The present study has applied and tested multiple algorithms to map the areas susceptible to wildfire in the Mediterranean Region of Turkey. Besides, the performance of XGBoost, CatBoost, Gradient Boost, AdaBoost, and LightGBM methods for wildfire susceptibility mapping is also examined. The results have revealed the higher testing accuracy of CatBoost (95.47%) algorithm, followed by LightGBM (94.70%), XGBoost (88.8%), AdaBoost (86.0%), and GBM (84.48%) algorithms. Resultant wildfire susceptibility maps provide proper inventories for forest engineers, planners, and local governments for future policies regarding disaster management in Turkey.
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ISSN:1687-8086
1687-8094
DOI:10.1155/2022/3959150