Modeling Earth Systems and Environment

, Volume 3, Issue 4, pp 1515–1527 | Cite as

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

  • Hasan Zabihi
  • Mohsen Alizadeh
  • Iris Vogeler
  • Anuar Ahmad
  • Mohamad Nor Said
  • Bahman Ramezani Gourabi
Original Article


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.


Spatial analysis Citrus production Artificial neural network Geographic information system 



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.

Compliance with ethical standards

Conflict of interest

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hasan Zabihi
    • 1
  • Mohsen Alizadeh
    • 2
  • Iris Vogeler
    • 3
  • Anuar Ahmad
    • 1
  • Mohamad Nor Said
    • 1
  • Bahman Ramezani Gourabi
    • 4
  1. 1.Department of Geoinformation, Faculty of Geoinformation and Real EstateUniversiti Teknologi Malaysia (UTM)Johor BahruMalaysia
  2. 2.Department of Urban and Regional Planning, Faculty of Built EnvironmentUniversiti Teknologi Malaysia (UTM)Johor BahruMalaysia
  3. 3.AgResearchRuakura Research CentreHamiltonNew Zealand
  4. 4.Department of Geography, Rasht BranchIslamic Azad UniversityRashtIran

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