Skip to main content

Advertisement

Log in

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

  • Original Paper
  • Published:
International Journal of Environmental Science and Technology Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Abrams MJ, Brown L, Lepley R, Sadowski P (1983) Remote sensing for porphyry copper deposits in Southern Arizona. Econ Geol 78:591–604

    Article  Google Scholar 

  • 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 1:432–712

    Google Scholar 

  • Alavi Panah K (2009) Application of remote sensing in the Earth sciences (Soil Science), 1st edn. Tehran University Press, Tehran

    Google Scholar 

  • 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):565–588

    Article  Google Scholar 

  • 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 180–184

  • Chavez PS, Berlin GL, Sowers LB (2011) Statistical method for selecting Landsat MSS ratios. J Appl Photogr Eng 8:23–30

    Google Scholar 

  • 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):35

    Article  Google Scholar 

  • 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 30:421–429

    Article  Google Scholar 

  • 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 51:110–127

    Google Scholar 

  • Jensen JR, Lulla K (1987) Introductory digital image processing: a remote sensing perspective

  • Lillesand T, Kiefer RW (2011) Remote sensing and interpretation of the aerial and satellite images. In: Malmirian H (ed) 3rd ed

  • Lillesand T, Kiefer RW, Chipman J (2014) Remote sensing and image interpretation. John Wiley & Sons

  • Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25(12):2365–2401

    Article  Google Scholar 

  • 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 40:89–99

    Article  CAS  Google Scholar 

  • 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 2:140–146

    Google Scholar 

  • 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. https://doi.org/10.22092/ijwmse.2017.108740

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Shojaei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arabi Aliabad, F., Shojaei, S., Zare, M. et al. Assessment of the fuzzy ARTMAP neural network method performance in geological mapping using satellite images and Boolean logic. Int. J. Environ. Sci. Technol. 16, 3829–3838 (2019). https://doi.org/10.1007/s13762-018-1795-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13762-018-1795-7

Keywords

Navigation