Skip to main content
SpringerLink
Log in
Book cover

Fuzzy Logic in Its 50th Year pp 161–174Cite as

Fuzzy Sets in Earth and Space Sciences

  • Chapter
  • First Online:

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 341))

Abstract

Earth science refers to the field of science dealing with planet Earth while space science pertains several scientific disciplines studying the upper atmosphere, space, and celestial bodies rather than Earth. The fuzzy set theory is one of the tools that has been recently used in the earth and space sciences. In this chapter, we review and analyze the papers utilizing fuzzy logic in earth and space science problems from Scopus database. The graphical and tabular illustrations are presented for the subject areas, publication years and sources of the papers on earth and space sciences.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Angelo, J.A.: Encyclopedia of Space and Astronomy. Facts on File, Inc., NY-USA (2006)

    Google Scholar 

  2. Amanullah, S., Sundaram, A., Narayanan, E.S.L.: Fuzzy set theory application in meteorology—rainfall modelling. Indian J. Environ. Prot. 25(8), 673–679 (2005)

    Google Scholar 

  3. Barry, J.M., Gathmann, T.P.: Application of fuzzy logic control to the IntelliSTARTM architecture. In: Proceedings of the 16th AAS Rocky Mountain Guidance and Control Conference on Advances in the Astronautical Sciences, vol. 81, pp. 423–430, Keystone-CO, USA (1993)

    Google Scholar 

  4. Barua, A., Khorasani, K.: Hierarchical fault diagnosis and fuzzy rule-based reasoning for satellites formation flight. IEEE Trans. Aerosp. Electron. Syst. 47(4), 2435–2456 (2011)

    Google Scholar 

  5. Cullen, K.E.: Earth Science: The People Behind the Science. Chelsea House, NY-USA (2006)

    Google Scholar 

  6. Demicco, R.V.: Fuzzy Logic and Earth Science: An Overview, Fuzzy Logic in Geology. Academic Press, London (2003)

    Google Scholar 

  7. Ding, Y., Han, H., Liu, F.: Intelligent integrated data processing model for oceanic warning system. Knowl. Based Syst. 23(1), 61–69 (2010)

    Article  Google Scholar 

  8. Feather, R.M., Snyder, S.L. : Earth Science. McGraw-Hill, Glencoe (1999)

    Google Scholar 

  9. Furfaro, R., Dohm, J.M., Fink, W., Kargel, J., Schulze-Makuch, D., Fairen, A.G., Palmero-Rodriguez, A., Baker, V.R., Ferre, P.T., Hare, T.M., Tarbell, M.A., Miyamoto, H., Komatsu, G.: The search for life beyond Earth through fuzzy expert systems. Planet. Space Sci. 56, 448–472 (2008)

    Article  Google Scholar 

  10. Glumov, V.M., Krutova, I.N.: A fuzzy logic adaptation circuit for control systems of deformable space vehicles: its design. Autom. Remote C 63(7), 1109–1122 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  11. Guo, H., Wu, J.: Space Science & Technology in China: A Roadmap to 2050. Science Press Beijing, ISBN 978-7-03-025703-1,(2010)

    Google Scholar 

  12. Heidari, M., Nabavi, S.H., Shamshirband, S.: Application of an adaptive neural-fuzzy system to establish a relationship among nonlinear phenomena in meteorology to obtain monthly rainfall. In: ICSTE 2010 Proceedings of 2nd International Conference on Software Technology and Engineering, vol. 2, pp. 232–235 (2010)

    Google Scholar 

  13. Huang, C.: Fuzzy Logic and Earthquake Research, Fuzzy Logic in Geology. Academic Press, London (2003)

    Google Scholar 

  14. Kaya, İ., Kahraman, C.: Process capability analyses with fuzzy parameters. Expert Syst. Appl. 38, 11918–11927 (2011)

    Article  Google Scholar 

  15. Kim, S.-W., Park, S.-Y., Park, C.-D.: Preliminary test of adaptive neuro-fuzzy inference system controller for spacecraft attitude control. J. Astron. Space Sci. 29(4), 389–395 (2002)

    Article  Google Scholar 

  16. Kumar, J.K., Konno, M., Yasuda, N.: Subsurface soil-geology interpolation using fuzzy neural network. J. Geotech. Geoenviron. Eng. 126(7), 632–639 (2000)

    Article  Google Scholar 

  17. Li, J.-X., Zhang, R., Jin, B.-G., Wang, H.-Z.: Possibility estimation of generating internal waves in the northwest Pacific Ocean using the fuzzy logic technique. J. Marine Sci. Technol. 20(2), 237–244 (2012)

    Google Scholar 

  18. Lu, D.-W., Wu, L.R., Li, Z.-X.: The evaluation of mine geology disasters based on fuzzy mathematics and grey theory. J. Coal Sci. Eng. 13(4), 480–483 (2007)

    Google Scholar 

  19. Lu, Y.-D., Zhang, J., Li, M.-S.: GIS-based evaluation of urban environmental engineering geology by fuzzy logic: a case study of Lüeyang County, Shaanxi Province. J. Nat. Disasters 14(2), 93–98 (2005)

    Google Scholar 

  20. Monroe, J.S., Wicander, R.: The Changing Earth: Exploring Geology and Evolution, 7th edn. Cengage Learning, CT-USA (2015)

    Google Scholar 

  21. Moradi, M., Esmaelzadeh, R., Ghasemi, A.: Adjustable adaptive fuzzy attitude control using nonlinear SISO structure of satellite dynamics. T. Jpn. Soc. Aeronaut. S 55(5), 265–273 (2012)

    Article  Google Scholar 

  22. Morello, E.B., Galibert, G., Smith, D., Ridgway, K.R., Howell, B., Slawinski, D., Timms, G.P., Evans, K., Lynch, T.P.: Quality control (QC) procedures for Australia’s National Reference Station’s sensor data-comparing semi-autonomous systems to an expert oceanographer. Methods Oceanogr. 9, 17–33 (2014)

    Article  Google Scholar 

  23. Nichols, C.R., Williams, R.G.: Encyclopedia of Marine Science. Facts on File, Inc., NY-USA (2009)

    Google Scholar 

  24. Omar, H.M.: Designing integrated fuzzy guidance law for aerodynamic homing missiles using genetic algorithms. T. Jpn. Soc. Aeronaut. S 53(180), 99–104 (2010)

    Article  Google Scholar 

  25. Piedra-Fernandez, J.A., Ortega, G., Wang, J.Z., Canton-Garbin, M.: Fuzzy content-based image retrieval for oceanic remote sensing. EEE Trans. Geosci. Remote Sens. 52(9), 5422–5431 (2014)

    Article  Google Scholar 

  26. Potyupkin, A.Y.: Application of a fuzzy measure in problems of monitoring the technical state of space vehicles. Meas. Tech. 45(7), 689–695 (2002)

    Article  Google Scholar 

  27. Rahoma, W.A., Rahoma, U.A., Hassan, A.H.: Application of neuro-fuzzy techniques for solar radiation. J. Comput. Sci. 7(10), 1605–1611 (2011)

    Article  Google Scholar 

  28. Sathiya, R.D., Vaithiyanathan, V., Suraj, M.S., Venkatraman, G.B., Sathivel, P.: Assessing the wave heights of the ocean using neural networks and fuzzy logic. In: Proceedings of 2013 International Conference on Emerging Trends in VLSI, Embedded System, Nano Electronics and Telecommunication System, ICEVENT 2013, Tiruvannamalai, Tamil Nadu, India (2013)

    Google Scholar 

  29. Schneider, P.: Extragalactic Astronomy and Cosmology. Springer-Verlag Berlin Heidelberg, (2015)

    Google Scholar 

  30. Shamir, L., Nemiroff, R.J.: Astronomical pipeline processing using fuzzy logic. Appl. Soft Comput. J. 8(1), 79–87 (2008)

    Article  Google Scholar 

  31. Shioiri, H., Ueno, S.: Three-dimensional collision avoidance control law for aircraft using risk function and fuzzy logic. T. Jpn. Soc. Aeronaut. S 47(154), 253–261 (2004)

    Article  Google Scholar 

  32. Srivastava, G., Panda, S.N., Mondal, P., Liu, J.: Forecasting of rainfall using ocean-atmospheric indices with a fuzzy neural technique. J. Hydrol. 395(3–4), 190–198 (2010)

    Google Scholar 

  33. Sun, J., Li, Y.: Multidomain petrophysically constrained inversion and geology differentiation using guided fuzzy c-means clustering. Geophysics 80(4) (2014)

    Google Scholar 

  34. Sylaios, G., Bouchette, F., Tsihrintzis, V.A., Denamiel, C.: A fuzzy inference system for wind-wave modelling. Ocean Eng. 36(17–18), 1358–1365 (2009)

    Article  Google Scholar 

  35. Valdés, J.J., Bonham-Carter, G.: Time dependent neural network models for detecting changes of state in complex processes: applications in earth sciences and astronomy. Neural Netw. 19(2), 196–207 (2006)

    Article  MATH  Google Scholar 

  36. Vidal-Fernandez, E., Piedra-Fernandez, J.A., Almendros-Jimenez, J.M., Canton-Garbin, M.: OBIA system for identifying mesoscale oceanic structures in SeaWiFS and MODIS-aqua images. IEEE J. Sel. Top. Appl. 8(3), 1256–1265 (2015)

    Google Scholar 

  37. Wang, P., Zhao, X., Yang, D.: Fixed thrust optimal-fuzzy combined control for spacecraft formation flying. T. Jpn. Soc. Aeronaut. S 47(155), 9–16 (2004)

    Article  Google Scholar 

  38. Wang, Y., Li, Y., Liu, W., Gao, Y.: Assessing operational ocean observing equipment (OOOE) based on the fuzzy comprehensive evaluation method. Ocean Eng. 107, 54–59 (2015)

    Article  Google Scholar 

  39. Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  40. Zhang Y.,Cao Y.: A fuzzy quantification approach of uncertainties in an extreme wave height modeling. Acta. Oceanologica. Sinica. 34(3), 90–98 (2015)

    Google Scholar 

  41. Zhang, R., Yin, L., Gu, H.: Environment perception for AUV in uncertain ocean environment. Jisuanji Yanjiu yu Fazhan/Comput. Res. Dev. 50(9), 1981–1991 (2013)

    Google Scholar 

  42. Zuchora-Walske, C.: Science Discovery Timelines, Key Discoveries in Earth and Space Science. Lerner Publishing Group, Inc., MN-USA (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Managemet Engineering, Istanbul Technical University, 34367, Macka, Istanbul, Turkey

    Irem Otay

  2. Department of Industrial Engineering, Istanbul Technical University, 34367, Macka, Istanbul, Turkey

    Cengiz Kahraman

Authors
  1. Irem Otay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cengiz Kahraman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Irem Otay .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Uzay Kaymak

  3. Department of Computer Engineering, Middle East Technical University (M, Ankara, Turkey

    Adnan Yazici

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Otay, I., Kahraman, C. (2016). Fuzzy Sets in Earth and Space Sciences. In: Kahraman, C., Kaymak, U., Yazici, A. (eds) Fuzzy Logic in Its 50th Year. Studies in Fuzziness and Soft Computing, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-319-31093-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31093-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31091-6

  • Online ISBN: 978-3-319-31093-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation