A machine learning-based approach for wildfire susceptibility map**. The case study of the Liguria region in Italy

M Tonini, M D'Andrea, G Biondi, S Degli Esposti… - Geosciences, 2020 - mdpi.com
Wildfire susceptibility maps display the spatial probability of an area to burn in the future,
based solely on the intrinsic local proprieties of a site. Current studies in this field often rely …

Multi-criteria decision analysis for forest fire risk assessment by coupling AHP and GIS: Method and case study

N Nuthammachot, D Stratoulias - Environment, Development and …, 2021 - Springer
Fire is one of the main causes of environmental and ecosystem change. Geospatial data,
derived from satellite images and surveying observations, are a useful tool in managing land …

[HTML][HTML] Spatial map** and analysis of forest fire risk areas in Sri Lanka–Understanding environmental significance

RK Makumbura, P Dissanayake… - Case Studies in …, 2024 - Elsevier
This study presents the first attempt in Sri Lanka to generate a forest fire risk map covering
the entire country using a GIS-based forest fire index (FFI) model. The model utilized seven …

A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire …

H Hong, SA Naghibi, M Moradi Dashtpagerdi… - Arabian Journal of …, 2017 - Springer
Forest fire is known as an important natural hazard in many countries which causes financial
damages and human losses; thus, it is necessary to investigate different aspects of this …

Wildfire susceptibility map** using five boosting machine learning algorithms: The case study of the Mediterranean region of Turkey

SKM Abujayyab, MM Kassem, AA Khan… - Advances in Civil …, 2022 - Wiley Online Library
Forest fires caused by different environmental and human factors are responsible for the
extensive destruction of natural and economic resources. Modern machine learning …

[HTML][HTML] A comparison of two machine learning classification methods for remote sensing predictive modeling of the forest fire in the North-Eastern Siberia

P Janiec, S Gadal - Remote Sensing, 2020 - mdpi.com
The problem of forest fires in Yakutia is not as well studied as in other countries. Two
methods of machine learning classifications were implemented to determine the risk of fire …

[HTML][HTML] Fire risk probability map** using machine learning tools and multi-criteria decision analysis in the gis environment: A case study in the National Park Forest …

Y Maniatis, A Doganis, M Chatzigeorgiadis - Applied Sciences, 2022 - mdpi.com
Fire risk will increase in the upcoming years due to climate change. In this context, GIS
analysis for fire risk map** is an important tool to identify high risk areas and allocate …

[HTML][HTML] A model proposal for measuring performance in occupational health and safety in forest fires

AB Küçükarslan, M Köksal, I Ekmekci - Sustainability, 2023 - mdpi.com
This study endeavors to prioritize occupational health and safety (OHS) accomplishments
across ten forest management directorates in a specified province of Turkey, utilizing multi …

A GIS-and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand

N Nuthammachot, D Stratoulias - Geocarto International, 2021 - Taylor & Francis
Forest fires are abrupt transformations of the natural ecosystem and management authorities
are required to take preventive measures to tackle fire events. Geographic information …

Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods

D Arca, M Hacısalihoğlu, ŞH Kutoğlu - Natural Hazards, 2020 - Springer
Located in the Mediterranean basin, one of the world's leading places in terms of forest fires,
Turkey is one of the countries where forest fires are experienced very often due to both …