Assessment of the fuzzy ARTMAP neural network method performance in geological mapping using satellite images and Boolean logic

  • F. Arabi Aliabad
  • S. ShojaeiEmail author
  • M. Zare
  • M. R. Ekhtesasi
Original Paper


Currently, in executive comparative studies and even in the research studies of natural resources, the use of maps produced by the geological survey forms the basis of geological studies. However, in recent years, remote sensing technology has been introduced as a new and efficient tool for geological studies which in addition to the accuracy has other benefits such as access to the arduous mountainous areas or inaccessible that can help us to prepare geological maps with more accuracy. The purpose of this study is to compare geological survey geological maps with a scale of 1: 100,000, and produced and modified by Google Earth maps by using fuzzy ARTMAP artificial neural network method. For this purpose, the geological map of a part of Yazd-Shirkooh watershed prepared by using Landsat 7 image by fuzzy ARTMAP artificial neural network method with Kappa coefficient of 89%. Also, geological survey and Google Earth map prepared. Graphic map obtained from the visual interpretation of Google Earth images with the ground control was considered as the base map and comparing it with other maps evaluated by using Boolean logic. By using the sampling network created 100 points which 62 points were in the study area and 56 cases in the fuzzy ARTMAP map and 52 cases in the geological survey map were consistent with the base map. Due to reduced cost and time and no limits of time and space, fuzzy ARTMAP is a suitable method for the preparation of geological maps.


Fuzzy ARTMAP Remote sensing Shirkooh Boolean logic Geological map 



The authors wish to thank all who assisted in conducting this work.


  1. Abrams MJ, Brown L, Lepley R, Sadowski P (1983) Remote sensing for porphyry copper deposits in Southern Arizona. Econ Geol 78:591–604CrossRefGoogle Scholar
  2. Alami Nia Z, Karimpour MH, Heidarian Shahri MR (2010) Geology, alteration and geochemical studies in Kalateh Teymour area North-North East of Iran. J Econ Geol 1:432–712Google Scholar
  3. Alavi Panah K (2009) Application of remote sensing in the Earth sciences (Soil Science), 1st edn. Tehran University Press, TehranGoogle Scholar
  4. Carpenter GA, Grossberg S, Reynolds JH (1991) ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Netw 4(5):565–588CrossRefGoogle Scholar
  5. Chang SS, Zadeh LA (1996) On fuzzy mapping and control. In: Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh, pp 180–184Google Scholar
  6. Chavez PS, Berlin GL, Sowers LB (2011) Statistical method for selecting Landsat MSS ratios. J Appl Photogr Eng 8:23–30Google Scholar
  7. Dehghani AA, Bahremand AR, Shojaei S (2017) Intelligent estimation of flood hydrographs using an adaptive neuro–fuzzy inference system (ANFIS). Model Earth Syst Environ 3(1):35CrossRefGoogle Scholar
  8. Gil-Sáncheza L, Garriguesa J, Garcia-Breijoa E, Graub R, Marta A, Baigtsb D, Baratb JM (2015) Artificial neural networks (Fuzzy ARTMAP) analysis of the data obtained with an electronic tongue applied to a ham-curing process with different salt formulations. Appl Soft Comput 30:421–429CrossRefGoogle Scholar
  9. Honarmand MV, Ranjbar H (2004) The application of different image processing methods of ETM data to explore porphyry and vein copper deposits in Mamzar Mountain in Kerman province. Geosci J 51:110–127Google Scholar
  10. Jensen JR, Lulla K (1987) Introductory digital image processing: a remote sensing perspectiveGoogle Scholar
  11. Lillesand T, Kiefer RW (2011) Remote sensing and interpretation of the aerial and satellite images. In: Malmirian H (ed) 3rd edGoogle Scholar
  12. Lillesand T, Kiefer RW, Chipman J (2014) Remote sensing and image interpretation. John Wiley & SonsGoogle Scholar
  13. Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25(12):2365–2401CrossRefGoogle Scholar
  14. Ramadan MT, Kontny A (2004) Mineralogical and structural characterization of alteration zones detected by orbital remote sensing at Shalatein District, SEDesert, Egypt. J Afr Earth Sc 40:89–99CrossRefGoogle Scholar
  15. Tangestani MH, Moore F (2000) Iron-oxide and hydroxyl enhancement using the Crosta method: a case study from the Zagros Belt, Fars Province, Iran. J Appl Geosci 2:140–146Google Scholar
  16. Zare Shooraki P, Ekhtesasi MR, Golkarian A, Hosseini Z (2015) The effects of geological formations on groundwater quality with application of Boulian logic, case study: bajestan watershed plain. Watershed Eng Manag 9(1):11–21. Google Scholar

Copyright information

© Islamic Azad University (IAU) 2018

Authors and Affiliations

  • F. Arabi Aliabad
    • 1
  • S. Shojaei
    • 2
    Email author
  • M. Zare
    • 1
  • M. R. Ekhtesasi
    • 1
  1. 1.Department of Management the Arid and Desert RegionsYazd UniversityYazdIran
  2. 2.Young Researchers and Elite Club, Zahedan BranchIslamic Azad UniversityZahedanIran

Personalised recommendations