Land allocation based on spatial analysis using artificial neural networks and GIS in Ramsar, Iran
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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.
KeywordsSpatial 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.
- Alhoniemi E, Hollmén J, Simula O, Vesanto J (1999) Process monitoring and modeling using the self-organizing map. Integr Comput Aided Eng 6(1):3–14Google Scholar
- Bishop C (1995). Neural networks for pattern recognition. Clarendon Press, OxfordGoogle Scholar
- CDCGC (2004) Citrus and date crop germplasm Committee. USA. Citrus and Date Germplasm: Crop Vulnerability, Germplasm Activities, Germplasm Needs. Citrus and Date Crop Germplasm Committee, USA, pp 1–30Google Scholar
- Cho S, Han C, Han DH, Kim HI (2000) Web-based keystroke dynamics identity verification using neural network. J Organ Comput Electr Commer 10(4):295–307Google Scholar
- ESRI (2011) ArcGIS desktop: release 10. Environmental Systems Research Institute, RedlandsGoogle Scholar
- Gómez-Sanchis J, Blasco J, Soria-Olivas E, Lorente D, Escandell-Monteroa P, Martínez-Martínez JM, Martínez-Sober M, Aleixos N (2013) Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers. Postharvest Biol Technol 82:76–86CrossRefGoogle Scholar
- Haykin S (2009). Neural networks and learning machines. Prentice-Hall, Upper Saddle RiverGoogle Scholar
- Ji CY (2000). Land-use classification of remotely sensed data using Kohonen selforganizing feature map neural networks. Photogramm Eng Remote Sens 66:1451–1460Google Scholar
- Kadirgama K, Amirruddin AK, Bakar RA (2014) Estimation of solar radiation by artificial networks: east coast Malaysia. In: 2013 International Conference on Alternative Energy in Developing Countries and Emerging Economies, 52. Energy, Procedia, pp 383–388Google Scholar
- Kangas J, Simula O (1995) Process monitoring and visualization using self organizing map. In: Bulsari AB (ed) Neural networks for chemical engineers, vol 14. Elsevier Science, DordrechtGoogle Scholar
- Kumar J, Brooks B, Thornton P, Dietze M (2012) Sub-daily statistical downscaling of meteorological variables using neural networks. In: International Conference on Computational Science, ICCS 2012. Procedia Computer Science, 9, 887–896Google Scholar
- Pan G, Pan J (2012) Research in crop land suitability analysis based on GIS. Comput Comput Technol Agric 365:314–325Google Scholar
- Pijanowski BC, Tayyebi A, Doucette J, Pekin BK, Braun D, Plourde J (2014). A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environ Model Softw 51:250–268CrossRefGoogle Scholar