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Environmental science and pollution research international
Jan, 2023;30 (2): 4721-4732.

Mapping the forest fire risk zones using artificial intelligence with risk factors data.

Volkan Sevinç
Author Information
  1. Volkan Sevinç: Faculty of Science, Department of Statistics, Muğla Sıtkı Koçman University, 48000, Muğla, Turkey. vsevinc@mu.edu.tr. ORCID
PMID: 35974271 DOI: 10.1007/s11356-022-22515-w

Abstract

Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires, and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of 11 human-induced factors, a natural factor, and temperature, which is one of the risk factors that determine the conditions for the occurrence of forest fires. Moreover, k-means clustering algorithm, which is an artificial intelligence method, was employed in preparation of the fire risk zone map. Turkey was selected as the study area because there are social and economic variations among its regions. Thus, the regional forest directorates in Turkey were separated into four clusters as extreme-risk zone, high-risk zone, moderate-risk zone, and low-risk zone. Also, a map presenting these risk zones were provided. The map reveals that, in general, the western and southwestern coastal areas of Turkey are at high risk of forest fires. On the other hand, the fire risk is relatively low in the northern, central, and eastern areas.

Keywords

Artificial intelligence Forest fire risk zone map Forest fires K-means algorithm

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MeSH Term

Humans
Wildfires
Fires
Artificial Intelligence
Forests
Risk Factors
Trees
Journal Article
Article has an altmetric score of 1

Word Cloud

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