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Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey.

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Title: Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey.
Authors: Akıncı, Hazan Alkan1 (AUTHOR) hazan.akinci@artvin.edu.tr, Akıncı, Halil2 (AUTHOR)
Source: Earth Science Informatics. Mar2023, Vol. 16 Issue 1, p397-414. 18p.
Subject Terms: *Forest fire prevention & control, *Machine learning, *Landslides, *Artificial neural networks, *Receiver operating characteristic curves, *Forest fires, *Forest density, *Forest surveys
Geographic Terms: Turkey
Abstract: This study primarily aims to produce forest fire susceptibility maps for the Manavgat district of Antalya province in Turkey using different machine learning (ML) techniques. Forest fire inventory data were obtained from the General Directorate of Forestry. The inventory data comprise a total of 545 forest fire ignition points during the years 2013–2021. For model training and validation, 70% and 30% of these points were used, respectively. Average annual temperature, average annual rainfall, aspect, distance to rivers, elevation, distance to settlements, forest type, distance to roads, land cover, plan curvature, slope, solar radiation, tree cover density, topographic wetness index, and wind effect parameters were used in the study. Multicollinearity analysis of these 15 factors showed that they are independent of each other. Tree-based ML models, namely, eXtreme gradient boosting (XGBoost), random forest, and gradient boosting machine, as well as artificial neural networks (ANN) were used to produce forest fire susceptibility maps. The metrics of overall accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve (AU-ROC) were used to evaluate the performance of the ML models. Based on our results, the XGBoost model revealed the most appropriate susceptibility map that could be used for fire prevention measures. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 162013351
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  Label: Title
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  Data: Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey.
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  Data: <searchLink fieldCode="AR" term="%22Akıncı%2C+Hazan+Alkan%22">Akıncı, Hazan Alkan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hazan.akinci@artvin.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Akıncı%2C+Halil%22">Akıncı, Halil</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Earth+Science+Informatics%22">Earth Science Informatics</searchLink>. Mar2023, Vol. 16 Issue 1, p397-414. 18p.
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  Data: *<searchLink fieldCode="DE" term="%22Forest+fire+prevention+%26+control%22">Forest fire prevention & control</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Landslides%22">Landslides</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink><br />*<searchLink fieldCode="DE" term="%22Forest+fires%22">Forest fires</searchLink><br />*<searchLink fieldCode="DE" term="%22Forest+density%22">Forest density</searchLink><br />*<searchLink fieldCode="DE" term="%22Forest+surveys%22">Forest surveys</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Turkey%22">Turkey</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This study primarily aims to produce forest fire susceptibility maps for the Manavgat district of Antalya province in Turkey using different machine learning (ML) techniques. Forest fire inventory data were obtained from the General Directorate of Forestry. The inventory data comprise a total of 545 forest fire ignition points during the years 2013–2021. For model training and validation, 70% and 30% of these points were used, respectively. Average annual temperature, average annual rainfall, aspect, distance to rivers, elevation, distance to settlements, forest type, distance to roads, land cover, plan curvature, slope, solar radiation, tree cover density, topographic wetness index, and wind effect parameters were used in the study. Multicollinearity analysis of these 15 factors showed that they are independent of each other. Tree-based ML models, namely, eXtreme gradient boosting (XGBoost), random forest, and gradient boosting machine, as well as artificial neural networks (ANN) were used to produce forest fire susceptibility maps. The metrics of overall accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve (AU-ROC) were used to evaluate the performance of the ML models. Based on our results, the XGBoost model revealed the most appropriate susceptibility map that could be used for fire prevention measures. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s12145-023-00953-5
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 397
    Subjects:
      – SubjectFull: Forest fire prevention & control
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Landslides
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Receiver operating characteristic curves
        Type: general
      – SubjectFull: Forest fires
        Type: general
      – SubjectFull: Forest density
        Type: general
      – SubjectFull: Forest surveys
        Type: general
      – SubjectFull: Turkey
        Type: general
    Titles:
      – TitleFull: Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Akıncı, Hazan Alkan
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          Name:
            NameFull: Akıncı, Halil
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          Dates:
            – D: 01
              M: 03
              Text: Mar2023
              Type: published
              Y: 2023
          Identifiers:
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              Value: 18650473
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              Value: 16
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Earth Science Informatics
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