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Spatial interpolation of surface point velocity using an adaptive neuro-fuzzy inference system model: a comparative study

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Abstract

Surface displacement measurements of the earth’s crust using GNSS observations are a discrete form and occur at the location of stations. Therefore, it is not possible to study crustal deformation as a continuous field. To overcome this problem, we propose the idea of using an adaptive neuro-fuzzy inference system (ANFIS) model. In the new method, the geodetic coordinates of GPS stations are input vectors, and the components of the displacement field in two-dimensions (Ve, Vn) are used as an output. The new method is analyzed using the observations of 25 GPS stations located in the northwest of Iran. Due to ample GPS stations and a tectonically active area, this region has been selected for study. The results of the new model are compared with the GPS-observed results, and with results produced by three alternative interpolation processes, namely artificial neural network (ANN), Ordinary Kriging (OK) and polynomial velocity field. The root-mean-square error (RMSE), correlation coefficient and relative error are calculated for all four interpolation processes. In the testing step, the averaged RMSE of the ANN, ANFIS, OK, and polynomial models is 2.0, 1.6, 2.7 and 3.2 mm year. The estimated velocity field by the ANFIS has been converted to a strain field and compared to the strain obtained from GPS measurements. Comparing the modeled strains with the ANFIS and GPS output for two control stations shows a correlation coefficient of 0.94 between the new model and GPS. The results reveal the capability and efficiency of ANFIS in comparison with ANN, OK and polynomial models.

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Data availability

By contacting the corresponding author, all data can be provided to the readers. The IGS Rinex files can be downloaded from the ftp://cddis.gsfc.nasa.gov/pub/gps/data/daily.

References

  • Bogusz J, Klos A, Grzempowski P, Kontny B (2013) Modeling the velocity field in a regular grid in the area of Poland on the basis of the velocities of European permanent stations. Pure Appl Geophys 171(6):809–833. https://doi.org/10.1007/s00024-013-0645-2

    Article  Google Scholar 

  • Cakmakci M, Kinaci C, Bayramoğlu M, Yildirim Y (2010) A modeling approach for iron concentration in sand filtration effluent using adaptive neuro-fuzzy model. Expert Syst Appl 37(2):1369–1373

    Article  Google Scholar 

  • Chen R (1991) On the horizontal crustal deformations in Finland. Finish Geodetic Institute, Helsinki

    Google Scholar 

  • Dach R, Hugentobler U, Fridez P, Meindl M (2007) Bernese GPS software version 5.0. Astronomical Institute, University of Bern, Bern

    Google Scholar 

  • Djamour Y, Vernant P, Nankali H, Tavakoli F (2011) NW Iran-eastern Turkey present-day kinematics: results from the Iranian permanent GPS network. Earth Planet Sci Lett 307(1):27–34

    Article  Google Scholar 

  • Erdogan S (2010) Modeling the spatial distribution of DEM error with geographically weighted regression: an experimental study. Comput Geosci 36(1):34–43

    Article  Google Scholar 

  • Feizi R, Voosoghi B, Ghaffari RM, R, (2020) Regional modeling of the ionosphere using adaptive neuro-fuzzy inference system in Iran. Adv Space Res 65(11):2515–2528

    Article  Google Scholar 

  • Fernandez J et al (2018) Modeling the two- and three-dimensional displacement field in Lorca, Spain, subsidence and the global implications. Sci Rep 8(1):14782

    Article  Google Scholar 

  • Ghaderpour E, Pagiatakis SD, Hassan QK (2021) A survey on change detection and time series analysis with applications. Appl Sci 11(13):6141. https://doi.org/10.3390/app11136141

    Article  Google Scholar 

  • Ghaffari Razin MR, Voosoghi B (2020) Ionosphere time series modeling using adaptive neuro-fuzzy inference system and principal component analysis. GPS Solut 24(2):24–51

    Article  Google Scholar 

  • Ghaffari Razin MR, Voosoghi B, Mohammadzadeh A (2015) Efficiency of artificial neural networks in map of total electron content over Iran. Acta Geod Geophys 51(3):541–555

    Article  Google Scholar 

  • Grafarend EW, Voosoghi B (2003) Intrinsic deformation analysis of the earth’s surface based on displacement fields derived from space geodetic measurements. Case studies: present-day deformation patterns of Europe and of the Mediterranean area (ITRF data sets). J Geod 77(5):303–326

    Article  Google Scholar 

  • Gullu M, Yilmaz I, Yilmaz M, Turgut B (2011) An alternative method for estimating densification point velocity based on back propagation artificial neural networks. Stud Geophys Geod 55(1):73–86

    Article  Google Scholar 

  • Hossainal MM, Becker M, Groten E (2010) Comprehensive approach to the analysis of the 3D kinematics deformation with application to the Kenai Peninsula. J Geod Sci 1(1):59–73. https://doi.org/10.2478/v10156-010-0008-1

    Article  Google Scholar 

  • Hu J, LiZ SQ, Zhu J, Ding X (2012) Three-dimensional surface displacements from InSAR and GPS measurements with variance component estimation. IEEE Geosci Remote Sens Lett 9(4):754–758. https://doi.org/10.1109/LGRS.2011.2181154

    Article  Google Scholar 

  • Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  • Joseph VR (2006) Limit Kriging. Technometrics 48(4):458–466

    Article  Google Scholar 

  • Konakoglu B (2021) Prediction of geodetic point velocity using MLPNN, GRNN, and RBFNN models: a comparative study. Acta Geod Geophys 56(2):271–291. https://doi.org/10.1007/s40328-021-00336-6

    Article  Google Scholar 

  • Li J, Heap AD (2008) A review of spatial interpolation methods for environmental scientists. Geoscience Australia, Canberra

  • Liang H, Zhan W, Li J (2021) Vertical surface displacement of mainland China from GPS using the multi-surface function method. Adv Space Res 68(12):4898–4915. https://doi.org/10.1016/j.asr.2021.02.024

    Article  Google Scholar 

  • Malekshahian Z, Raoofian Naeeni M (2018) Deformation analysis of Iran Plateau using intrinsic geometry approach and C1 finite element interpolation of GPS observations. J Geodyn 119(2018):47–61

    Article  Google Scholar 

  • Matheron G (1971) The theory of regionalized variables and its applications. Centre de Geostatistique, Fontainebleau Paris

  • Moghtased-Azar K, Zaletnyik P (2009) Crustal velocity field modeling with neural network and polynomials. In: Sideris MG (ed) Observing our changing, earth international association of geodesy symposia, pp 809–816

  • Raeesi M, Zarifi Z, Nilfouroushan F, Boroujeni S, Tiampo K (2017) Quantitative analysis of seismicity in Iran. Pure Appl Geophys 174(3):793–833

    Article  Google Scholar 

  • Rastbood A, Vosooghi B (2012) Study of tectonic plate motions contribution of the middle-east region in the GPS velocity field of Iranian campaign global geodynamic network scientific quarterly journal. Geosciences 21(84):15–24

    Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RG (1986) Learning internal representations by error propagation. In: Parallel distributed processing, MIT Press, Cambridge, pp 318–362

  • Segal P, Matthews MV (1988) Displacement calculations from geodetic data and the testing of geophysical deformation models. J Geophys Res 93(B12):14954–14966

    Article  Google Scholar 

  • Voosoghi B (2000) Intrinsic deformation analysis of the earth surface based on 3-D displacement fields derived from space geodetic measurements, PhD thesis, Department of Geodesy and Geoinformatics, Stuttgart University

  • Yetilmezsoy K (2019) Applications of soft computing methods in environmental engineering. In: Hussain C (ed) Handbook of environmental materials management. Springer, Cham

    Google Scholar 

  • Yilmaz M (2013) Artificial neural networks pruning approach for geodetic velocity field determination. BCG Boletim De Ciências Geodésicas 19:558–573

    Article  Google Scholar 

  • Yilmaz M, Gullu M (2014) A comparative study for the estimation of geodetic point velocity by artificial neural networks. J Earth Syst Sci 123(4):791–808

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank the reviewers for providing very valuable and scientific comments. The National Cartographic Center (NCC) of Iran and the International GNSS Service (IGS) are also thanked for providing the required data.

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Authors and Affiliations

  1. Department of Geoscience Engineering, Arak University of Technology, Arāk, Iran

    Seyyed Reza Ghaffari-Razin & Navid Hooshangi

  2. Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    Asghar Rastbood

Authors
  1. Seyyed Reza Ghaffari-Razin
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  2. Asghar Rastbood
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  3. Navid Hooshangi
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Contributions

S.R. Ghaffari Razin initiated the study, provided the Matlab source codes for analysis, collected and analyzed the data. A. Rastbood and N. Hooshangi analyzed the part of data and wrote the manuscript. All authors helped to shape the analysis and manuscript. All authors reviewed the manuscript.

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Correspondence to Seyyed Reza Ghaffari-Razin.

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Ghaffari-Razin, S.R., Rastbood, A. & Hooshangi, N. Spatial interpolation of surface point velocity using an adaptive neuro-fuzzy inference system model: a comparative study. GPS Solut 27, 30 (2023). https://doi.org/10.1007/s10291-022-01374-5

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