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Land allocation based on spatial analysis using artificial neural networks and GIS in Ramsar, Iran

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Abstract

The purpose of the current study is to compare two kinds of allocation maps. In this investigation, the first map is taken from a supervised classification of the advanced spaceborne thermal emission and reflection radiometer imagery, and the other map is adopted from topo-climatic data assessment; the self-organizing map (SOM) and multi-layer perception (MLP). Topo-climatic data were analysed through artificial neural network (ANN) techniques as it has allowed not only to detect to distinct between low, moderate and high allocation zones. A new computational framework was developed in this research to compare results between two different methods including SOM and MLP. In this field, geographic information system (GIS) is applied due to the ability of GIS databases to integrate and work with information from heterogeneous and uncertain data into a geospatial context. The results show that the MLP was significantly close to current cultivation. Yet, it has provided better insights compared to the SOM in safe regions with regard to citrus allocation maps (CAMs). An accuracy assessment of 99.8% demonstrated the allocation of the proposed approach. Consequently, the comparison and differences of SOM and MLP algorithm of ANN method along with the topo-climatic factors could help policymakers and planners to improve the accuracy of CAMs.

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Acknowledgements

The authors would like to thank to Citrus and Subtropical Fruits research Center, Ramsar, Iran and the Department of Geoinformation in Universiti Teknologi Malaysia (UTM) to prepare opportunity and providing facilities for this investigation.

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Authors and Affiliations

  1. Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Skudai, 81310, Johor Bahru, Johor, Malaysia

    Hasan Zabihi, Anuar Ahmad & Mohamad Nor Said

  2. Department of Urban and Regional Planning, Faculty of Built Environment, Universiti Teknologi Malaysia (UTM), 81310, Johor Bahru, Johor, Malaysia

    Mohsen Alizadeh

  3. AgResearch, Ruakura Research Centre, Hamilton, New Zealand

    Iris Vogeler

  4. Department of Geography, Rasht Branch, Islamic Azad University, Rasht, Iran

    Bahman Ramezani Gourabi

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  1. Hasan Zabihi
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  2. Mohsen Alizadeh
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  4. Anuar Ahmad
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  5. Mohamad Nor Said
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  6. Bahman Ramezani Gourabi
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Correspondence to Hasan Zabihi.

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The authors declare no competing financial interest. We have read and understood the policy on declaration of interests and declare that we have no competing interests; no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.

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Zabihi, H., Alizadeh, M., Vogeler, I. et al. Land allocation based on spatial analysis using artificial neural networks and GIS in Ramsar, Iran. Model. Earth Syst. Environ. 3, 1515–1527 (2017). https://doi.org/10.1007/s40808-017-0371-3

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  • DOI: https://doi.org/10.1007/s40808-017-0371-3

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