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.
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Angelo, J.A.: Encyclopedia of Space and Astronomy. Facts on File, Inc., NY-USA (2006)
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)
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)
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)
Cullen, K.E.: Earth Science: The People Behind the Science. Chelsea House, NY-USA (2006)
Demicco, R.V.: Fuzzy Logic and Earth Science: An Overview, Fuzzy Logic in Geology. Academic Press, London (2003)
Ding, Y., Han, H., Liu, F.: Intelligent integrated data processing model for oceanic warning system. Knowl. Based Syst. 23(1), 61–69 (2010)
Feather, R.M., Snyder, S.L. : Earth Science. McGraw-Hill, Glencoe (1999)
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)
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)
Guo, H., Wu, J.: Space Science & Technology in China: A Roadmap to 2050. Science Press Beijing, ISBN 978-7-03-025703-1,(2010)
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)
Huang, C.: Fuzzy Logic and Earthquake Research, Fuzzy Logic in Geology. Academic Press, London (2003)
Kaya, İ., Kahraman, C.: Process capability analyses with fuzzy parameters. Expert Syst. Appl. 38, 11918–11927 (2011)
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)
Kumar, J.K., Konno, M., Yasuda, N.: Subsurface soil-geology interpolation using fuzzy neural network. J. Geotech. Geoenviron. Eng. 126(7), 632–639 (2000)
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)
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)
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)
Monroe, J.S., Wicander, R.: The Changing Earth: Exploring Geology and Evolution, 7th edn. Cengage Learning, CT-USA (2015)
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)
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)
Nichols, C.R., Williams, R.G.: Encyclopedia of Marine Science. Facts on File, Inc., NY-USA (2009)
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)
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)
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)
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)
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)
Schneider, P.: Extragalactic Astronomy and Cosmology. Springer-Verlag Berlin Heidelberg, (2015)
Shamir, L., Nemiroff, R.J.: Astronomical pipeline processing using fuzzy logic. Appl. Soft Comput. J. 8(1), 79–87 (2008)
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)
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)
Sun, J., Li, Y.: Multidomain petrophysically constrained inversion and geology differentiation using guided fuzzy c-means clustering. Geophysics 80(4) (2014)
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)
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)
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)
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)
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)
Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Zhang Y.,Cao Y.: A fuzzy quantification approach of uncertainties in an extreme wave height modeling. Acta. Oceanologica. Sinica. 34(3), 90–98 (2015)
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)
Zuchora-Walske, C.: Science Discovery Timelines, Key Discoveries in Earth and Space Science. Lerner Publishing Group, Inc., MN-USA (2015)
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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
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