Skip to main content

Advertisement

SpringerLink
Log in

Artificial intelligence models to generate visualized bedrock level: a case study in Sweden

  • Original Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

A Correction to this article was published on 02 July 2020

This article has been updated

Abstract

Assessment of the spatial distribution of bedrock level (BL) as the lower boundary of soil layers is associated with many uncertainties. Increasing our knowledge about the spatial variability of BL through high resolution and more accurate predictive models is an important challenge for the design of safe and economical geostructures. In this paper, the efficiency and predictability of different artificial intelligence (AI)-based models in generating improved 3D spatial distributions of the BL for an area in Stockholm, Sweden, were explored. Multilayer percepterons, generalized feed-forward neural network (GFFN), radial based function, and support vector regression (SVR) were developed and compared to ordinary kriging geostatistical technique. Analysis of the improvement in progress using confusion matrixes showed that the GFFN and SVR provided closer results to realities. The ranking of performance accuracy using different statistical errors and precision–recall curves also demonstrated the superiority and robustness of the GFFN and SVR compared to the other models. The results indicated that in the absence of measured data the AI models are flexible and efficient tools in creating more accurate spatial 3D models. Analyses of confidence intervals and prediction intervals confirmed that the developed AI models can overcome the associated uncertainties and provide appropriate prediction at any point in the subsurface of the study area.

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

Access this article

Log in via an institution

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Change history

  • 02 July 2020

    In the original version of this article, unfortunately a character of the Journal no in the reference 10 has been published incorrectly.

References

  • Abbaszadeh Shahri A (2016) An optimized artificial neural network structure to predict clay sensitivity in a high landslide prone area using piezocone penetration test (CPTu) data—a case study in southwest of Sweden. Geotech Geol Eng 34(2):745–758. https://doi.org/10.1007/s10706-016-9976-y

    Article  Google Scholar 

  • Abbaszadeh Shahri A, Asheghi R (2018) Optimized developed artificial neural network-based models to predict the blast-induced ground vibration. Innov Infrastruct Solut. https://doi.org/10.1007/s41062-018-0137-4

    Article  Google Scholar 

  • Abbaszadeh Shahri A, Esfandiyari B, Hamzeloo H (2011) Evaluation of a nonlinear seismic geotechnical site response analysis method subjected to earthquake vibrations (case study: Kerman Province, Iran). Arab J Geosci 4(7–8):1103–1116. https://doi.org/10.1007/s12517-009-0120-7

    Article  Google Scholar 

  • Abbaszadeh Shahri A, Larsson S, Johansson F (2015) CPT-SPT correlations using artificial neural network approach: a case study in Sweden. Electron J Geotech Eng 20(28):13439–13460

    Google Scholar 

  • Acar E (2014) Simultaneous optimization of shape parameters and weight factors in ensemble of radial basis functions. Struct Multidiscip Optim 49(6):969–978. https://doi.org/10.1007/s00158-013-1028-0

    Article  Google Scholar 

  • Adler RJ (1981) The geometry of random fields. Wiley, New York

    Google Scholar 

  • Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185. https://doi.org/10.1080/00031305.1992.10475879

    Article  Google Scholar 

  • Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces, principles of data mining and knowledge discovery. Lecture Notes in Computer Science. Springer, Berlin, p 15. https://doi.org/10.1007/3-540-45681-3_2

    Book  Google Scholar 

  • Arulampalam G, Bouzerdoum A (2002) Expanding the structure of shunting inhibitory artificial neural network classifiers. In: IJCNN. IEEE. https://doi.org/10.1109/IJCNN.2002.1007601

  • Asheghi R, Abbaszadeh Shahri A, Zak Khorsand (2019) Prediction of strength index parameters of different rock types using hybrid multi output intelligence model. Arab J Sci Eng 44(10):8645–8659. https://doi.org/10.1007/s1336-019-04046-8

    Article  Google Scholar 

  • Asheghi R, Hosseini SA, Saneie M, Abbaszadeh Shahri A (2020) Updating the neural network sediment load models using different sensitivity analysis methods: a regional application. J Hydroinform. https://doi.org/10.2166/hydro.2020.098

    Article  Google Scholar 

  • Azimi H, Bonakdari H, Ebtehaj I, Michelson DG (2018) A combined adaptive neuro-fuzzy inference system–firefly algorithm model for predicting the roller length of a hydraulic jump on a rough channel bed. Neural Comput Appl 29:249–258. https://doi.org/10.1007/s00521-016-2560-9

    Article  Google Scholar 

  • Baecher GB (1986) Geotechnical error analysis. Transportation Research Record, Washington, D.C, vol 1105, pp 23–31

  • Bennett KP, Mangasarian OL (1992) Robust linear programming discrimination of two linearly inseparable sets. Optim Methods Softw 1:23–34

    Article  Google Scholar 

  • Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355

    Google Scholar 

  • Cerato AB, Lutenegger AJ (2007) Scale effects of shallow foundation bearing capacity on granular material. J Geotech Geoenviron Eng ASCE 133(10):1192–1202

    Article  Google Scholar 

  • Chang JR, Hung CT, Chen DH (2006) Application of an artificial neural network on depth to bedrock prediction. Int J Comput Intell Res (IJCIR) 2(1):33–40. https://doi.org/10.5019/j.ijcir.2006.39

    Article  Google Scholar 

  • Chew LP (1989) Constrained delaunay triangulations. Algorithmica 4(1–4):97–108

    Article  Google Scholar 

  • Chiles JP, Delfiner P (1999) Geostatistics: modelling spatial uncertainty. Wiley series in probability and statistics. Wiley, New York

    Book  Google Scholar 

  • Christakos G (1992) Random field models in earth sciences. Academic Press, San Diego

    Google Scholar 

  • Christakos G (2000) Modern spatiotemporal geostatistics. Oxford University Press, New York

    Google Scholar 

  • Clarke GKC, Berthier E, Schoof CG, Jarosch AH (2009) Neural networks applied to estimating subglacial topography and glacier volume. J Clim 22:2146–2160

    Article  Google Scholar 

  • Coomans D, Massart DL (1982) Alternative k-nearest neighbour rules in supervised pattern recognition, part 1. K-Nearest neighbour classification by using alternative voting rules. Anal Chim Acta 136:15–27. https://doi.org/10.1016/S0003-2670(01)95359-0

    Article  Google Scholar 

  • Courant R, Hilbert D (1953) Methods of mathematical physics. Interscience, Newo York

    Google Scholar 

  • Cracknell MJ, Reading AM (2014) Geological mapping using remote sensing data: a comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Comput Geosci 63:22–33

    Article  Google Scholar 

  • Cressie NAC (1991) Statistics for spatial data. Wiley, New York

    Google Scholar 

  • De Carneiro CC, Fraser SJ, Crósta AP, Silva AM, de Barros CEM (2012) Semiautomated geologic mapping using self-organizing maps and airborne geophysics in the Brazilian Amazon. Geophysics 77:K17–K24

    Article  Google Scholar 

  • Deutsch CV (1996) Correcting for negative weights in ordinary kriging. Comput Geosci 22(7):765–773. https://doi.org/10.1016/0098-3004(96)00005-2

    Article  Google Scholar 

  • Domiter V, Zalik B (2008) Sweep-line algorithm for constrained delaunay triangulation. Int J Geogr Inf Sci 22(4):449–462

    Article  Google Scholar 

  • Dowd PA, Pardo-Iguzquiza E (2005) Estimating the boundary surface between geologic formations from 3D seismic data using neural networks and geostatistics. Geophysics 70(1):1–11. https://doi.org/10.1190/1.1852783

    Article  Google Scholar 

  • European commission (2005) GOPA, Belgium. https://doi.org/10.1190/1.1852783, www.smartcities-infosystem.eu

  • Everitt BS, Landau S, Leese M, Stahl D (2011) Miscellaneous clustering methods, in cluster analysis. Wiley, Chichester

    Book  Google Scholar 

  • Fenton GA (1999) Random field modeling of CPT data. J Geotech Geoenviron Eng 125(6):486–498. https://doi.org/10.1061/(ASCE)1090-0241(1999)125:6(486)

    Article  Google Scholar 

  • Frank T, Tertois AL, Mallet JL (2007) 3D-reconstruction of complex geological interfaces from irregularly distributed and noisy point data. Comput Geosci 33(7):932–943

    Article  Google Scholar 

  • Garcia S, Derrac J, Cano J, Herrera F (2012) Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans Pattern Anal Mach Intell 34(3):417–435. https://doi.org/10.1109/tpami.2011.142

    Article  Google Scholar 

  • Garrido-Marijuan A, Pargova Y, Wilson C (2017) The making of a smart city: best practices across Europe. EU samart cities information system. Empowering smart solutions for better cities

  • Gelfand AE, Diggle P, Guttorp P, Fuentes M (2010) Handbook of spatial statistics. CRC Press, Boca Raton

    Book  Google Scholar 

  • Ghaderi A, Abbaszadeh Shahri A, Larsson S (2019) An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu). Bull Eng Geol Environ. https://doi.org/10.1007/s10064-018-1400-9

    Article  Google Scholar 

  • Hall P, Park BU, Samworth RJ (2008) Choice of neighbor order in nearest-neighbor classification. Ann Stat 36(5):2135–2152. https://doi.org/10.1214/07-AOS537

    Article  Google Scholar 

  • Held M (2001) FIST: fast industrial-strength triangulation of polygons. Algorithmica 30(4):563–596

    Article  Google Scholar 

  • Hengl TA (2009) A practical guide to geostatistical mapping. University of Amsterdam, Amsterdam

    Google Scholar 

  • Hsu CW, Chang CC, Lin CJ (2010) A practical guide to support vector classification. Department of Computer Science and Information Systems, National Taiwan University, Taipei. www.csie.ntu.edu.tw/_cjlin/libsvm

  • Im HK, Stein ML, Zhu Z (2007) Semiparametric estimation of spectral density with irregular observations. J Am Stat Assoc 102:726–735

    Article  Google Scholar 

  • Isaaks EH, Srivastava RM (1990) An introduction to applied geostatistics. Oxford University Press, Oxford

    Google Scholar 

  • Jadhav S, Nalbalwar S, Ghatol A (2012) Performance evaluation of generalized feedforward neural network based ECG arrhythmia classifier. Int J Comput Sci Issues 9(4):379–384

    Google Scholar 

  • Krige DG (1951) A statistical approach to some basic mine valuation problems on the Witwatersrand. J Chem Metall Min Soc S Afr 52(6):119–139

    Google Scholar 

  • Manandhar S, Cho HI, Kim DS (2016) Effect of bedrock stiffness and thickness of weathered rock on response spectrum in Korea. KSCE J Civ Eng 20(7):2677–2691. https://doi.org/10.1007/s12205-016-0811-z

    Article  Google Scholar 

  • Maus S (1999) Variogram analysis of magnetic and gravity data. Geophysics 64(3):776–784

    Article  Google Scholar 

  • Mei G, Tipper JC, Xu N (2013) Ear-clipping based algorithms of generating high-quality polygon triangulation. In: Proceedings of the international conference on information technology and software engineering, vol 212. Springer, pp 979–988

  • Mey J, Scherler D, Zeilinger G, Strecker MR (2015) Estimating the fill thickness and bedrock topography in intermontane valleys using artificial neural networks. J Geophys Res Earth Surf 120:1301–1320. https://doi.org/10.1002/2014JF003270

    Article  Google Scholar 

  • Miller J, Franklin J, Aspinall R (2007) Incorporating spatial dependence in predictive vegetation models. Ecol Model 202(3–4):225–242

    Article  Google Scholar 

  • Min J, Lee Y (2005) Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Exp Syst Appl 28(4):603–614. https://doi.org/10.1016/j.eswa.2004.12.008

    Article  Google Scholar 

  • Nath RR, Kumar G, Sharma ML, Gupta SC (2018) Estimation of bedrock depth for a part of Garhwal Himalayas using two different geophysical techniques. Geosci Lett 5:9. https://doi.org/10.1186/s40562-018-0108-9

    Article  Google Scholar 

  • O’Sullivan D, Unwin DJ (2010) Geographics information analysis. Book chapter, knowing the unknowable: the statistics of field. Wiley, New York, pp 292–311. https://doi.org/10.1002/9780470549094.ch10

    Book  Google Scholar 

  • Papritz A (2009) Limitations of indicator kriging for predicting data with trend. In: StatGIS conference proceedings. Milos, Greece, pp 1–6

  • Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3(2):246–257. https://doi.org/10.1162/neco.1991.3.2.246

    Article  Google Scholar 

  • Pérez-Cruz F, Navia-Vázquez A, Alarcón-Diana PL, ArtésRodríguez A (2000a) An IRWLS procedure for SVR. In: Proceedings of the EUSIPCO, Tampere, Finland

  • Pérez-Cruz F, Alarcón-Diana PF, Navia-Vázquez A, ArtésRodríguez A (2000b) Fast training of support vector classifiers. In: Leen T, Dietterich T, Tresp V (eds) Neural information processing systems, vol 13. MIT Press, Cambridge, pp 734–740

    Google Scholar 

  • Pfaffhuber AA, Lysdahl AO, Christensen CW, Vöge M, Kjennbakken H, Mykland J (2019) Extraction of depth of bedrock from airborn electromagnetic data using artificial neural networks. In: SAGEEP 2019, 32nd annual symposium on the application of geophysics to engineering and environmental, Portland, Oregon, USA

  • Powers DMW (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Technol 2(1):37–63

    Google Scholar 

  • Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, SIGMOD ‘00. https://doi.org/10.1145/342009.335437

  • Reich BJ (2012) Spatiotemporal quantile regression for detecting distributional changes in environmental processes. J R Stat Soc Ser C 61:535–553

    Article  Google Scholar 

  • Sahoo S, Jha MK (2017) Pattern recognition in lithology classification: modeling using neural networks, self-organizing maps and genetic algorithms. Hydrogeol J 25:311–330

    Article  Google Scholar 

  • Samui P, Sitharam TG (2011) Application of geostatistical models for estimating spatial variability of rock depth. Engineering 3:886–894. https://doi.org/10.4236/eng.2011.39108

    Article  Google Scholar 

  • Samworth RJ (2012) Optimal weighted nearest neighbour classifiers. Ann Stat 40(5):2733–2763. https://doi.org/10.1214/12-AOS1049

    Article  Google Scholar 

  • Schwenker F, Kestler HA, Günther P (2001) Three learning phases for radial-basis-function networks. Neural Netw 14(4–5):439–458. https://doi.org/10.1016/s0893-6080(01)00027-2

    Article  Google Scholar 

  • Shahin M (2016) State-of-the-art review of some artificial intelligence applications in pile foundations. Geosci Front 7(1):33–44. https://doi.org/10.1016/j.gsf.2014.10.002

    Article  Google Scholar 

  • Shahin MA, Jaksa MB, Maier HR (2009) Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. Adv Artif Neural Syst. https://doi.org/10.1155/2009/308239

    Article  Google Scholar 

  • Skøien JO, Merz R, Bloschl G (2005) Top-kriging—geostatistics on stream networks. Hydrol Earth Syst Sci Discuss 2:2253–2286. https://doi.org/10.5194/hessd-2-2253-2005

    Article  Google Scholar 

  • Stehman S (1997) Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ 62(1):77–89

    Article  Google Scholar 

  • Stein ML (1999) Interpolation of spatial data: some theory for Kriging. Springer, New York

    Book  Google Scholar 

  • Tacher L, Pomian-Srzednicki I, Parriaux A (2006) Geological uncertainties associated with 3-D subsurface models. Comput Geosci 32:212–221

    Article  Google Scholar 

  • Toussaint GT (2005) Geometric proximity graphs for improving nearest neighbor methods in instance-based learning and data mining. Int J Comput Geom Appl 15(2):101–150. https://doi.org/10.1142/S0218195905001622

    Article  Google Scholar 

  • Tuia D, Verrelst J, Alonso L, Pérez-Cruz F, Camps-Valls G (2011) Multi output support vector regression for remote sensing biophysical parameter estimation. IEEE Geosci Remote Sens Lett 8(4):804–808

    Article  Google Scholar 

  • Uzielli M, Vannucchi G, Phoon KK (2005) Random filed characterization of strees-normalised cone penetration testing parameters. Geotechnique 55(1):3–20

    Article  Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory, 2nd edn. Springer, New York

    Book  Google Scholar 

  • Viswanathan R, Samui P (2015) Determination of rock depth using artificial intelligence techniques. Geosci Front. https://doi.org/10.1016/j.gsf.2015.04.002

    Article  Google Scholar 

  • Wang X (2019) Uncertainty quantification and reduction in the characterization of subsurface stratigraphy using limited geotechnical investigation data. Undergr Space. https://doi.org/10.1016/j.undsp.2018.10.008

    Article  Google Scholar 

  • Willmott CJ (1984) On the evaluation of model performance in physical geography. In: Gaetan C, Guyon X (eds) Spatial statistics and models. Springer, Berlin, pp 443–460

    Chapter  Google Scholar 

  • Xu N, Tian H (2009) Wireframe:a reliable approach to build sealed engineering geological models. Comput Geosci 35(8):1582–1591

    Article  Google Scholar 

  • Yamamoto JK (2005) Comparing ordinary kriging interpolation variance and indicator kriging conditional variance for assessing uncertainties at unsampled locations. In: Dessureault S, Ganguli R, Kecojevic V, Dwyer J (eds) Application of computers and operations research in the mineral industry. Balkema, Rotterdam

    Google Scholar 

  • Yan F, Shangguan W, Zhang J, Hu B (2018) Depth-to-bedrock map of China at a spatial resolution of 100 meters. Earth Syst Sci Data Discuss. https://doi.org/10.5194/essd-2018-103

    Article  Google Scholar 

  • Zhang H, Wang YJ, Li YF (2009) SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence. Min Sci Technol 19(3):385–388. https://doi.org/10.1016/S1674-5264(09)60072-7

    Article  Google Scholar 

Download references

Acknowledgements

We thank the Sollentuna community, WSP, and Johan Lundberg AB for supplying our data and Samuel Renkel and Angela Gremyr for their follow-up in getting permission to use the data. This work was a pilot for a larger research project funded by the Swedish Transport Administration, Better Interaction in Geotechnics (BIG), the Rock Engineering Research Foundation, and Tyréns AB.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Islamic Azad University, Roudehen Branch, Tehran, Iran

    Abbas Abbaszadeh Shahri

  2. Division of Soil and Rock Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden

    Stefan Larsson

  3. Johan Lundberg AB, Uppsala, Sweden

    Abbas Abbaszadeh Shahri & Crister Renkel

Authors
  1. Abbas Abbaszadeh Shahri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefan Larsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Crister Renkel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abbas Abbaszadeh Shahri.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abbaszadeh Shahri, A., Larsson, S. & Renkel, C. Artificial intelligence models to generate visualized bedrock level: a case study in Sweden. Model. Earth Syst. Environ. 6, 1509–1528 (2020). https://doi.org/10.1007/s40808-020-00767-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40808-020-00767-0

Keywords

Search

Navigation