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Spatial Analysis of Mountain and Lowland Anoa Habitat Potential Using the Maximum Entropy and Random Forest Algorithm

Author

Listed:
  • Diah Ardiani

    (Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Sukolilo, Surabaya 60111, Indonesia)

  • Lalu Muhamad Jaelani

    (Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Sukolilo, Surabaya 60111, Indonesia)

  • Septianto Aldiansyah

    (Department of Geography, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok 16424, Indonesia)

  • Mangapul Parlindungan Tambunan

    (Department of Geography, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok 16424, Indonesia)

  • Mochamad Indrawan

    (Department of Biology, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok 16424, Indonesia)

  • Andri A. Wibowo

    (Department of Biology, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok 16424, Indonesia)

Abstract

The Anoa is a wild animal endemic to Sulawesi that looks like a small cow. Anoa are categorized as vulnerable to extinction on the IUCN red list. There are two species of Anoa, namely Lowland Anoa ( Bubalus depressicornis ) and Mountain Anoa ( Bubalus quarlesi ). In this study, a comparison of potential habitat models for Anoa species was conducted using Machine Learning algorithms with the Maximum Entropy (MaxEnt) and Random Forest (RF) methods. This modeling uses eight environmental variables. Where based on the results of Bubalus quarlesi potential habitat modeling, the RF 75:25 model is the best algorithm with the highest variable contribution, namely humidity of 82.444% and a potential area of 5% of Sulawesi Island, with an Area Under Curve (AUC) of 0.987. Meanwhile, the best Bubalus depressicornis habitat potential model is the RF 70:30 algorithm, with the highest variable contribution, namely population of 88.891% and potential area of 36% of Sulawesi Island, with AUC 0.967. This indicates that Anoa extinction is very sensitive to the presence of humidity and human population levels.

Suggested Citation

  • Diah Ardiani & Lalu Muhamad Jaelani & Septianto Aldiansyah & Mangapul Parlindungan Tambunan & Mochamad Indrawan & Andri A. Wibowo, 2023. "Spatial Analysis of Mountain and Lowland Anoa Habitat Potential Using the Maximum Entropy and Random Forest Algorithm," World, MDPI, vol. 4(4), pages 1-17, October.
  • Handle: RePEc:gam:jworld:v:4:y:2023:i:4:p:41-669:d:1253056
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    Keywords

    Anoa; habitat potential; MaxEnt; RF;
    All these keywords.

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