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

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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.

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Authors and Affiliations

  1. Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran

    Manijeh Talebi, Baris Majnounian, Majid Makhdoum & Ehsan Abdi

  2. Department of Mechanical Engineering Agricultural Machinery, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

    Mahmoud Omid

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  1. Manijeh Talebi
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  2. Baris Majnounian
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  4. Ehsan Abdi
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Contributions

MT, BM, MM, EA, and MO helped in conceptualization; MT, BM, MM, EA, and MO contributed to methodology; MT and MO were involved in software; MT, BM, MM, EA, and MO helped in formal analysis; MT, BM, MM, EA, and MO helped in investigation; MT and MO were involved in data curation; MT was involved in writing—original draft preparation; MT, BM, EA, and MO contributed to writing—review and editing.

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Correspondence to Ehsan Abdi.

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Talebi, M., Majnounian, B., Makhdoum, M. et al. Predicting areas with ecotourism capability using artificial neural networks and linear discriminant analysis (case study: Arasbaran Protected Area, Iran). Environ Dev Sustain 23, 8272–8287 (2021). https://doi.org/10.1007/s10668-020-00964-y

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