Skip to main content
Log in

Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The prediction of the potential of soil liquefaction induced by the earthquake is a vital task in construction engineering and geotechnical engineering. To provide a possible solution to such problems, this paper proposes two support vector machine (SVM) models which are optimized by genetic algorithm (GA) and grey wolf optimizer (GWO) to predict the potential of soil liquefaction. Field observation data based on cone penetration test (CPT), standard penetration test (SPT) and shear wave velocity (VS) test (SWVT) are employed to verify the reliability of the GA–SVM model and the GWO–SVM model, the numbers of input variables of these three field testing data sets are 6, 12 and 8, respectively, and the output result is the potential of soil liquefaction. To verify whether the two optimization algorithms GA and GWO have significantly improved the performance of SVM model, an unoptimized SVM model is served as a reference in this study. And five performance metrics, including classification accuracy rate (ACC), precision rate (PRE), recall rate (REC), F1 score (F1) and AUC are used to evaluate the classification performance of the three models. Results of the study confirm that when CPT-based, SPT-based and SWVT-based test sets are input into three classification models, the highest classification accuracy of 0.9825, 0.9032 and 0.9231, respectively, is achieved with GWO–SVM. And based on these three data sets, the values of AUC obtained by GWO–SVM are all higher than those obtained by GA–SVM. Further, by comparing the other metrics of the three classification models, it is found that the classification performance of the two hybrid models is very similar and significantly better than the SVM, which indicates that GWO–SVM, like GA–SVM, can also be used as a reliable model for predicting soil liquefaction potential.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

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
Fig. 17

Similar content being viewed by others

References

  1. Samui P, Sitharam TG (2011) Machine learning modelling for predicting soil liquefaction susceptibility. Nat Hazard 11:1–9. https://doi.org/10.5194/nhess-11-1-2011

    Article  Google Scholar 

  2. Bhattacharya S, Hyodo M, Goda K et al (2011) Liquefaction of soil in the Tokyo Bay area from the 2011 Tohoku (Japan) earthquake. Soil Dyn Earthq Eng 31:1618–1628. https://doi.org/10.1016/j.soildyn.2011.06.006

    Article  Google Scholar 

  3. Samui P, Karthikeyan J (2013) Determination of liquefaction susceptibility of soil: a least square support vector machine approach. Int J Numer Anal Meth Geomech 37:1154–1161. https://doi.org/10.1002/nag.2081

    Article  Google Scholar 

  4. Zhao H-B, Ru Z-L, Yin S (2007) Updated support vector machine for seismic liquefaction evaluation based on the penetration tests. Mar Georesour Geotechnol 25:209–220. https://doi.org/10.1080/10641190701702303

    Article  Google Scholar 

  5. Juang CH, Chen CJ (2000) A rational method for development of limit state for liquefaction evaluation based on shear wave velocity measurements. Int J Numer Anal Meth Geomech 24:1–27

    Article  Google Scholar 

  6. Juang CH, Chen CJ, Tang WH et al (2000) CPT-based liquefaction analysis, Part 1: Determination of limit state function. Geotechnique 50:583–592. https://doi.org/10.1680/geot.2000.50.5.583

    Article  Google Scholar 

  7. Juang CH, Yuan HM, Lee DH et al (2003) Simplified cone penetration test-based method for evaluating liquefaction resistance of soils. J Geotech Geoenviron Eng 129:66–80. https://doi.org/10.1061/(asce)1090-0241(2003)129:1(66)

    Article  Google Scholar 

  8. Seo M-W, Olson SM, Sun C-G et al (2012) Evaluation of liquefaction potential index along western coast of South Korea Using SPT and CPT. Mar Georesour Geotechnol 30:234–260. https://doi.org/10.1080/1064119x.2011.614322

    Article  Google Scholar 

  9. Hoang ND, Bui DT (2018) Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study. Bull Eng Geol Env 77:191–204. https://doi.org/10.1007/s10064-016-0924-0

    Article  Google Scholar 

  10. Pal M (2006) Support vector machines-based modelling of seismic liquefaction potential. Int J Numer Anal Meth Geomech 30:983–996. https://doi.org/10.1002/nag.509

    Article  MATH  Google Scholar 

  11. Zhang JF, Wang YH (2021) An ensemble method to improve prediction of earthquake-induced soil liquefaction: a multi-dataset study. Neural Comput Appl 33:1533–1546. https://doi.org/10.1007/s00521-020-05084-2

    Article  Google Scholar 

  12. Hanna AM, Ural D, Saygili G (2007) Evaluation of liquefaction potential of soil deposits using artificial neural networks. Eng Comput 24:5–16. https://doi.org/10.1108/02644400710718547

    Article  Google Scholar 

  13. Zhou J, Li XB, Shi XZ (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Saf Sci 50:629–644. https://doi.org/10.1016/j.ssci.2011.08.065

    Article  Google Scholar 

  14. Shi XZ, Zhou J, Wu BB et al (2012) Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Trans Nonferrous Met Soc China 22:432–441. https://doi.org/10.1016/s1003-6326(11)61195-3

    Article  Google Scholar 

  15. Zhou J, Li XB, Mitri HS (2015) Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Nat Hazards 79:291–316. https://doi.org/10.1007/s11069-015-1842-3

    Article  Google Scholar 

  16. Hoang ND, Pham AD (2016) Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: a multinational data analysis. Expert Syst Appl 46:60–68. https://doi.org/10.1016/j.eswa.2015.10.020

    Article  Google Scholar 

  17. Sharma LK, Vishal V, Singh TN (2017) Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties. Measurement 102:158–169. https://doi.org/10.1016/j.measurement.2017.01.043

    Article  Google Scholar 

  18. Khandelwal M, Marto A, Fatemi SA et al (2018) Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples. Eng Comput 34:307–317. https://doi.org/10.1007/s00366-017-0541-y

    Article  Google Scholar 

  19. Zhou J, Li EM, Yang S et al (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518. https://doi.org/10.1016/j.ssci.2019.05.046

    Article  Google Scholar 

  20. Zhou J, Koopialipoor M, Li EM et al (2020) Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system. Bull Eng Geol Env 79:4265–4279. https://doi.org/10.1007/s10064-020-01788-w

    Article  Google Scholar 

  21. Zhou J, Asteris PG, Armaghani DJ et al (2020) Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dyn Earthq Eng 139:106390. https://doi.org/10.1016/j.soildyn.2020.106390

    Article  Google Scholar 

  22. Zhou J, Qiu Y, Zhu S et al (2020) Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization. Underground Space. https://doi.org/10.1016/j.undsp.2020.05.008

    Article  Google Scholar 

  23. Guo H, Zhou J, Koopialipoor M et al (2021) Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Eng Comput 37:173–186. https://doi.org/10.1007/s00366-019-00816-y

    Article  Google Scholar 

  24. Zhou J, Li C, Arslan CA et al (2021) Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Eng Comput 37:265–274. https://doi.org/10.1007/s00366-019-00822-0

    Article  Google Scholar 

  25. Yu Z, Shi XZ, Zhou J et al (2021) Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models. Eng Comput 37:991–1006. https://doi.org/10.1007/s00366-019-00868-0

    Article  Google Scholar 

  26. Wang S-m, Zhou J, Li C-q et al (2021) Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques. J Central South University 28:527–542. https://doi.org/10.1007/s11771-021-4619-8

    Article  Google Scholar 

  27. Qiu Y, Zhou J, Khandelwal M et al (2021) Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration. Eng Comput https://doi.org/https://doi.org/10.1007/s00366-021-01393-9

  28. Wei W, Li X, Liu J et al (2021) Performance Evaluation of Hybrid WOA-SVR and HHO-SVR Models with Various Kernels to Predict Factor of Safety for Circular Failure Slope. Applied Sciences-Basel 11. doi:https://doi.org/10.3390/app11041922

  29. Zhou J, Qiu Y, Armaghani DJ et al (2021) Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques. Geoscience Frontiers 12(3), 101091

  30. Hanna AM, Ural D, Saygili G (2007) Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dyn Earthq Eng 27:521–540. https://doi.org/10.1016/j.soildyn.2006.11.001

    Article  Google Scholar 

  31. Samui P, Kim D, Sitharam TG (2011) Support vector machine for evaluating seismic-liquefaction potential using shear wave velocity. J Appl Geophys 73:8–15. https://doi.org/10.1016/j.jappgeo.2010.10.005

    Article  Google Scholar 

  32. Venkatesh K, Kumar V, Tiwari RP (2013) Appraisal of liquefaction potential using neural network and neuro fuzzy approach. Appl Artif Intell 27:700–720. https://doi.org/10.1080/08839514.2013.823326

    Article  Google Scholar 

  33. Muduli PK, Das SK, Bhattacharya S (2014) CPT-based probabilistic evaluation of seismic soil liquefaction potential using multi-gene genetic programming. Georisk 8:14–28. https://doi.org/10.1080/17499518.2013.845720

    Article  Google Scholar 

  34. Xue X, Yang X, Li P (2017) Application of a probabilistic neural network for liquefaction assessment. Neural Network World 27:557–567. https://doi.org/10.14311/NNW.2017.27.030

    Article  Google Scholar 

  35. Xue X, Liu E (2017) Seismic liquefaction potential assessed by neural networks. Environ Earth Sci 76. doi:https://doi.org/10.1007/s12665-017-6523-y

  36. Zhou J, Li EM, Wang MZ et al (2019) Feasibility of Stochastic Gradient Boosting Approach for Evaluating Seismic Liquefaction Potential Based on SPT and CPT Case Histories. J Perform Constr Facil 33. doi:https://doi.org/10.1061/(asce)cf.1943-5509.0001292

  37. Alobaidi MH, Meguid MA, Chebana F (2019) Predicting seismic-induced liquefaction through ensemble learning frameworks. Sci Rep 9. doi:https://doi.org/10.1038/s41598-019-48044-0

  38. Ramakrishnan D, Singh TN, Purwar N et al (2008) Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data, Gujarat, India. Comput Geosci 12:491–501. https://doi.org/10.1007/s10596-008-9088-8

    Article  Google Scholar 

  39. Erzin Y, Ecemis N (2015) The use of neural networks for CPT-based liquefaction screening. Bull Eng Geol Env 74:103–116. https://doi.org/10.1007/s10064-014-0606-8

    Article  Google Scholar 

  40. Shahri AA (2016) Assessment and prediction of liquefaction potential using different artificial neural network models: a case study. Geotech Geol Eng 34:807–815. https://doi.org/10.1007/s10706-016-0004-z

    Article  Google Scholar 

  41. Chern S-G, Lee C-Y, Wang C-C (2008) CPT-based liquefaction assessment by using fuzzy-neural network. J Mar Sci Technol 16:139–148

    Article  Google Scholar 

  42. Xue X, Yang X (2013) Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction. Nat Hazards 67:901–917. https://doi.org/10.1007/s11069-013-0615-0

    Article  Google Scholar 

  43. Kohestani VR, Hassanlourad M, Ardakani A (2015) Evaluation of liquefaction potential based on CPT data using random forest. Nat Hazards 79:1079–1089. https://doi.org/10.1007/s11069-015-1893-5

    Article  Google Scholar 

  44. Hu JL, Tang XW, Qiu JN (2016) Assessment of seismic liquefaction potential based on Bayesian network constructed from domain knowledge and history data. Soil Dyn Earthq Eng 89:49–60. https://doi.org/10.1016/j.soildyn.2016.07.007

    Article  Google Scholar 

  45. Hu JL, Liu HB (2019) Bayesian network models for probabilistic evaluation of earthquake-induced liquefaction based on CPT and V-s databases. Eng Geol 254:76–88. https://doi.org/10.1016/j.enggeo.2019.04.003

    Article  Google Scholar 

  46. Young-Su K, Byung-Tak K (2006) Use of artificial neural networks in the prediction of liquefaction resistance of sands. J Geotech Geoenviron Eng 132:1502–1504. https://doi.org/10.1061/(asce)1090-0241(2006)132:11(1502)

    Article  Google Scholar 

  47. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  48. Goh ATC, Goh SH (2007) Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data. Comput Geotech 34:410–421. https://doi.org/10.1016/j.compgeo.2007.06.001

    Article  Google Scholar 

  49. Khandelwal M (2010) Evaluation and prediction of blast-induced ground vibration using support vector machine. Int J Rock Mech Min Sci 47:509–516. https://doi.org/10.1016/j.ijrmms.2010.01.007

    Article  Google Scholar 

  50. Khatibinia M, Fadaee MJ, Salajegheh J et al (2013) Seismic reliability assessment of RC structures including soil-structure interaction using wavelet weighted least squares support vector machine. Reliab Eng Syst Saf 110:22–33. https://doi.org/10.1016/j.ress.2012.09.006

    Article  Google Scholar 

  51. Kang F, Xu Q, Li JJ (2016) Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Appl Math Model 40:6105–6120. https://doi.org/10.1016/j.apm.2016.01.050

    Article  MathSciNet  MATH  Google Scholar 

  52. Zhou J, Li XB, Mitri HS (2016) Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods. J Comput Civil Eng 30. doi:https://doi.org/10.1061/(asce)cp.1943-5487.0000553

  53. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  54. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  55. Muro C, Escobedo R, Spector L et al (2011) Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav Processes 88:192–197. https://doi.org/10.1016/j.beproc.2011.09.006

    Article  Google Scholar 

  56. Xue XH, Xiao M (2016) Application of genetic algorithm-based support vector machines for prediction of soil liquefaction. Environ Earth Sci 75. doi:https://doi.org/10.1007/s12665-016-5673-7

  57. Kayen R, Moss RES, Thompson EM et al (2013) Shear-wave velocity-based probabilistic and deterministic assessment of seismic soil liquefaction potential. J Geotech Geoenviron Eng 139:407–419. https://doi.org/10.1061/(asce)gt.1943-5606.0000743

    Article  Google Scholar 

  58. Zhou ZY, Zhang RX, Wang YM et al (2018) Color difference classification based on optimization support vector machine of improved grey wolf algorithm. Optik 170:17–29. https://doi.org/10.1016/j.ijleo.2018.05.096

    Article  Google Scholar 

  59. Hou KY, Shao GH, Wang HM et al (2018) Research on practical power system stability analysis algorithm based on modified SVM. Prot Control Mod Power Syst 3. doi:https://doi.org/10.1186/s41601-018-0086-0

  60. Zhao HB (2008) Slope reliability analysis using a support vector machine. Comput Geotech 35:459–467. https://doi.org/10.1016/j.compgeo.2007.08.002

    Article  Google Scholar 

  61. Zhou T, Lu HL, Wang WW et al (2019) GA-SVM based feature selection and parameter optimization in hospitalization expense modeling. Appl Soft Comput 75:323–332. https://doi.org/10.1016/j.asoc.2018.11.001

    Article  Google Scholar 

  62. Yang XS (2010) A New Metaheuristic bat-inspired algorithm. In: Gonzalez JR, Pelta DA, Cruz C et al (eds) Nicso 2010: Nature inspired cooperative strategies for optimization, vol 284. Studies in computational intelligence. Springer-Verlag Berlin, Berlin, pp 65–74. doi:https://doi.org/10.1007/978-3-642-12538-6_6

  63. Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31:231–240. https://doi.org/10.1016/j.eswa.2005.09.024

    Article  Google Scholar 

  64. Pai PF (2006) System reliability forecasting by support vector machines with genetic algorithms. Math Comput Model 43:262–274. https://doi.org/10.1016/j.mcm.2005.02.008

    Article  MathSciNet  MATH  Google Scholar 

  65. Tao PY, Sun Z, Sun ZX (2018) An Improved intrusion detection algorithm based on GA and SVM. IEEE Access 6:13624–13631. https://doi.org/10.1109/access.2018.2810198

    Article  Google Scholar 

  66. Zhao M, Fu C, Ji L et al (2011) Feature selection and parameter optimization for support vector machines: a new approach based on genetic algorithm with feature chromosomes. Expert Syst Appl 38:5197–5204. https://doi.org/10.1016/j.eswa.2010.10.041

    Article  Google Scholar 

  67. Ruan JH, Jiang H, Li XY et al (2019) A Granular GA-SVM predictor for big data in agricultural cyber-physical systems. IEEE Trans Ind Inform 15:6510–6521. https://doi.org/10.1109/tii.2019.2914158

    Article  Google Scholar 

  68. Bian XQ, Zhang Q, Zhang L et al (2017) A grey wolf optimizer-based support vector machine for the solubility of aromatic compounds in supercritical carbon dioxide. Chem Eng Res Des 123:284–294. https://doi.org/10.1016/j.cherd.2017.05.008

    Article  Google Scholar 

  69. Armaghani DJ, Koopialipoor M, Bahri M et al (2020) A SVR-GWO technique to minimize flyrock distance resulting from blasting. Bull Eng Geol Env 79:4369–4385. https://doi.org/10.1007/s10064-020-01834-7

    Article  Google Scholar 

  70. Yu Z, Shi XZ, Zhou J et al (2020) Prediction of blast-induced rock movement during bench blasting: use of gray wolf optimizer and support vector regression. Nat Resour Res 29:843–865. https://doi.org/10.1007/s11053-019-09593-3

    Article  Google Scholar 

  71. Wei Y, Ni N, Liu DY et al (2017) An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major. Math Probl Eng 2017. doi:https://doi.org/10.1155/2017/9316713

  72. Bian XQ, Zhang L, Du ZM et al (2018) Prediction of sulfur solubility in supercritical sour gases using grey wolf optimizer-based support vector machine. J Mol Liq 261:431–438. https://doi.org/10.1016/j.molliq.2018.04.070

    Article  Google Scholar 

  73. Kaveh A, Hamze-Ziabari SM, Bakhshpoori T (2018) Patient rule-induction method for liquefaction potential assessment based on CPT data. Bull Eng Geol Env 77:849–865. https://doi.org/10.1007/s10064-016-0990-3

    Article  Google Scholar 

  74. Rahbarzare A, Azadi M (2019) Improving prediction of soil liquefaction using hybrid optimization algorithms and a fuzzy support vector machine. Bull Eng Geol Env 78:4977–4987. https://doi.org/10.1007/s10064-018-01445-3

    Article  Google Scholar 

  75. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874. https://doi.org/10.1016/j.patrec.2005.10.010

    Article  Google Scholar 

  76. Pandiyan V, Caesarendra W, Tjahjowidodo T et al (2018) In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. J Manuf Process 31:199–213. https://doi.org/10.1016/j.jmapro.2017.11.014

    Article  Google Scholar 

  77. Li C, Zhou J, Armaghani DJ, Cao W, Yagiz S (2021) Stochastic assessment of hard rock pillar stability based on the geological strength index system. Geomech Geophys Geo-Energy Geo-Resour 7(2):47. https://doi.org/10.1007/s40948-021-00243-8

    Article  Google Scholar 

  78. Li E, Zhou J, Shi X, Armaghani DJ, Yu Z, Chen X, Huang P (2020) Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill. Eng Comput 1–22. https://doi.org/10.1007/s00366-020-01014-x

  79. Zhou J, Qiu Y, Zhu S et al (2021) Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Eng Appl Artif Intell 97:104015. https://doi.org/10.1016/j.engappai.2020.104015

    Article  Google Scholar 

  80. Saito T, Rehmsmeier M (2015) The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One 10(3):e0118432. https://doi.org/10.1371/journal.pone.0118432

    Article  Google Scholar 

  81. Zhou J, Shi XZ, Huang RD, Qiu XY, Chen C (2016) Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines. Trans Nonferrous Metals Soc China 26(7):1938–1945

    Article  Google Scholar 

  82. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manage 45:427–437. https://doi.org/10.1016/j.ipm.2009.03.002

    Article  Google Scholar 

  83. Zhou J, Shi X, Li X (2016) Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining. J Vib Control 22(19):3986–3997

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by the Innovation‐Driven Project of Central South University (2020CX040) and the Shenghua Lieying Program of Central South University (Principle Investigator: Dr. Jian Zhou).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jian Zhou or Mingzheng Wang.

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

Zhou, J., Huang, S., Wang, M. et al. Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation. Engineering with Computers 38 (Suppl 5), 4197–4215 (2022). https://doi.org/10.1007/s00366-021-01418-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-021-01418-3

Keywords

Navigation