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Predicting areas with ecotourism capability using artificial neural networks and linear discriminant analysis (case study: Arasbaran Protected Area, Iran)

Author

Listed:
  • Manijeh Talebi

    (University of Tehran)

  • Baris Majnounian

    (University of Tehran)

  • Majid Makhdoum

    (University of Tehran)

  • Ehsan Abdi

    (University of Tehran)

  • Mahmoud Omid

    (University of Tehran)

Abstract

In this study, the common systematic approach in Iran as well as a multilayer perceptron neural network were used to evaluate the ecological capability of the area for ecotourism. The performance of the artificial neural network (ANN) and linear discriminant analysis (LDA) method in the prediction and ranking of areas with ecotourism capability were also compared. Based on the results obtained, the ANN with an overall accuracy of 97% outperformed LDA (overall accuracy of 86%) in terms of the prediction and classification of recreational areas. Therefore, for each class, the ANN with an accuracy, precision, and sensitivity of 98%, 94.33%, and 86.67%, respectively, outperformed the LDA with the corresponding values of 90.67%, 55.33%, and 40.33%, respectively. Based on the ANN-modeled map, 0.17%, 10.09%, and 89.74% of the area were shown to belong to intensive recreation class 2, extensive recreation class 2, and the not suitable for recreation class, respectively. Therefore, the ANN functions well with higher accuracy for modeling and classification of areas with ecotourism capability compared to LDA.

Suggested Citation

  • Manijeh Talebi & Baris Majnounian & Majid Makhdoum & Ehsan Abdi & Mahmoud Omid, 2021. "Predicting areas with ecotourism capability using artificial neural networks and linear discriminant analysis (case study: Arasbaran Protected Area, Iran)," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 8272-8287, June.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:6:d:10.1007_s10668-020-00964-y
    DOI: 10.1007/s10668-020-00964-y
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    References listed on IDEAS

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