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Data-Driven Modeling of Groundwater Level with Least-Square Support Vector Machine and Spatial–Temporal Analysis

  • Yandong Tang
  • Cuiping Zang
  • Yong Wei
  • Minghui Jiang
Original Paper
  • 34 Downloads

Abstract

Investigation of groundwater level is considered a prominent research topic for the study of underground hydrologic system. Due to the complexities of underground geological structure, the accuracy of real-time ground water level prediction is limited. In this study, a novel two-phase data-driven framework to model the time-series groundwater level with spatial–temporal analysis and least square support vector machine is proposed. Groundwater data collected from four monitoring sites in the northern region of United Kingdom is utilize in this study. In phase I, the time-series analysis is conducted to study the temporal characteristics of the groundwater. Based on the time-series analysis, least square support vector machine is performed to construct the prediction model to forecast the future groundwater level. In phase-II, the spatial correlation between the water levels in four sites are computed to construct a comprehensive model regarding the interrelation between the monitoring sites. Computational results illustrated the outperformance of least square support vector machine in predicting time-series groundwater levels compared with other state-of-arts machine learning algorithms. It has been demonstrated that the spatial–temporal model may serve as an applicable approach for the future research of groundwater resources.

Keywords

Groundwater level Spatial–temporal analysis Time-series analysis LS-SVM 

References

  1. Abrahart RJ, Anctil F, Coulibaly P, Dawson CW, Mount NJ, See LM, Shamseldin AY, Solomatine DP, Toth E, Wilby RL (2012) Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Prog Phys Geogr 36(4):480–513CrossRefGoogle Scholar
  2. Ahmadi A, Venayagamoorthy GK, Sharma R (2014) Performance of a smart microgrid with battery energy storage system’s size and state of charge. In: IEEE symposium on computational intelligence applications in smart grid, Orlando, FLGoogle Scholar
  3. Anibas C, Buis K, Verhoeven R, Meire P, Batelaan O (2011) A simple thermal mapping method for seasonal spatial patterns of groundwater–surface water interaction. J Hydrol 397:93–104CrossRefGoogle Scholar
  4. Castelletti A, Galelli S, Restelli M, Soncini-Sessa R (2012) Data-driven dynamic emulation modelling for the optimal management of environmental systems. Environ Model Softw 34:30–43CrossRefGoogle Scholar
  5. Chang FJ, Chang LC, Huang CW, Feng Kao I (2016) Prediction of monthly regional groundwater levels through hybrid soft-computing techniques. J Hydrol 541:965–976CrossRefGoogle Scholar
  6. Chua KS (2003) Efficient computations for large least square support vector machine classifiers. Pattern Recogn Lett 24(1–3):75–80CrossRefGoogle Scholar
  7. Corradini C, Flammini A, Morbidelli R, Govindaraju RS (2011) A conceptual model for infiltration in two-layered soils with a more permeable upper layer: from local to field scale. J Hydrol 410:62–72CrossRefGoogle Scholar
  8. Cui S, Wang G, Pei X, Huang R, Kamai T (2017) On the initiation and movement mechanisms of a catastrophic landslide triggered by the 2008 Wenchuan (Ms 8.0) earthquake in the epicenter area. Landslides 14(3):805–819CrossRefGoogle Scholar
  9. Cui S, Pei X, Huang R (2018) Effects of geological and tectonic characteristics on the earthquake-triggered Daguangbao landslide. China. Landslides 15(4):649–667CrossRefGoogle Scholar
  10. Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309(1–4):229–240CrossRefGoogle Scholar
  11. He Y, Kusiak A (2018) Performance assessment of wind turbines: data-derived quantitative metrics. IEEE Trans Sustain Energy 9(1):65–73CrossRefGoogle Scholar
  12. He Y, Deng J, Li H (2017) Short-term power load forecasting with deep belief network and copula models. In: 2017 9th international conference on intelligent human–machine systems and cybernetics (IHMSC), vol 1, pp 191–194Google Scholar
  13. He Y, Kusiak A, Ouyang T, Teng W (2017b) Data-driven modeling of truck engine exhaust valve failures: a case study. J Mech Sci Technol 31(6):2747–2757CrossRefGoogle Scholar
  14. He Y, Fei F, Wang W, Song X, Sun Z, Baek S (2018b) Predicting manufactured shapes of a projection micro-stereolithography process via convolutional encoder-decoder networks. In: Proceedings of ASME 2018 international design engineering technical conferences and computers and information in engineering conference, Quebec City, Quebec, CanadaGoogle Scholar
  15. Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Appl 13(4):18–28CrossRefGoogle Scholar
  16. Jones AL, Smart PL (2005) Spatial and temporal changes in the structure of groundwater nitrate concentration time series (1935–1999) as demonstrated by autoregressive modelling. J Hydrol 310(1–4):201–215CrossRefGoogle Scholar
  17. Kim KJ (2003) Financial time series forecasting using support vector machines. Neurocomputing 55(1–2):307–319CrossRefGoogle Scholar
  18. Li H, Feng W, Xu Q, He Y, Luo B, Chen S (2017) A revised formula to compute shear strength of unsaturated soils. Int J Georesources Environ 3(1–2):47–55CrossRefGoogle Scholar
  19. Li H, Xu Q, He Y, Deng J (2018) Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models. Landslides 15(10):2047–2059CrossRefGoogle Scholar
  20. Liu Y, Sheng Z (2011) Trend-outflow method for understanding interactions of surface water with groundwater and atmospheric water for eight reaches of the Upper Rio Grande. J Hydrol 409:710–723CrossRefGoogle Scholar
  21. McLeod A, Li W (1983) Diagnostic checking ARMA time series models using squared-residual autocorrelations. J Time Ser Anal 4(4):269–273CrossRefGoogle Scholar
  22. Mohanty S, Jha MK, Raul SK, Panda RK, Sudheer KP (2015) Using artificial neural network approach for simultaneous forecasting of weekly groundwater levels at multiple sites. Water Resour Manag 29(15):5521–5532CrossRefGoogle Scholar
  23. Naik N, Kominers SD, Raskar R, Glaeser EL, Hidalgo CA (2017) Computer vision uncovers predictors of physical urban change. Proc Natl Acad Sci 114(29):7571–7576CrossRefGoogle Scholar
  24. Ouyang T, Kusiak A, He Y (2017a) Modeling wind-turbine power curve: a data partitioning and mining approach. Renew Energy 102:1–8CrossRefGoogle Scholar
  25. Ouyang T, Kusiak A, He Y (2017b) Predictive model of yaw error in a wind turbine. Energy 123:119–130CrossRefGoogle Scholar
  26. Ouyang T, He Y, Li H, Sun Z, Baek, S (2017c) A deep learning framework for short-term power load forecasting. arXiv preprint arXiv:1711.11519
  27. Pelckmans K, Suykens JA, Van Gestel T, De Brabanter J, Lukas L, Hamers B, De Moor B, Vandewalle J (2002) LS-SVMlab: a matlab/c toolbox for least squares support vector machines. Tutorial. KULeuven-ESAT. Leuven, Belgium 142:1–2Google Scholar
  28. Safavi HR, Esmikhani M (2013) Conjunctive use of surface water and groundwater: application of support vector machines (SVMs) and genetic algorithms. Water Resour Manage 27(7):2623–2644CrossRefGoogle Scholar
  29. Shen HY, Chang LC (2013) Online multistep-ahead inundation depth forecasts by recurrent NARX networks. Hydrol Earth Syst Sci 17(3):935–945CrossRefGoogle Scholar
  30. Suykens JA, Lukas L, Van Dooren P, De Moor B, Vandewalle J (1999) Least squares support vector machine classifiers: a large scale algorithm. In: 1999 European conference on circuit theory and design, vol 99, pp 839–842Google Scholar
  31. Venayagamoorthy G, Sharma R, Gautam P, Ahmadi A (2016) Dynamic Energy management system for a smart microgrid. IEEE Trans Neural Netw Learn Syst 27(8):1643–1656CrossRefGoogle Scholar
  32. Wang H, Hu D (2015) Comparison of SVM and LS-SVM for regression. In: 2005 international conference in neural networks and brain, vol 1, pp 279–283Google Scholar
  33. Xu K, Xie M, Tang LC, Ho SL (2003) Application of neural networks in forecasting engine systems reliability. Appl Soft Comput 2(4):255–268CrossRefGoogle Scholar
  34. Xu Q, Li H, He Y, Liu F, Peng D (2017) Comparison of data-driven models of loess landslide runout distance estimation. Bull Eng Geol Environ.  https://doi.org/10.1007/s10064-017-1176-3 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yandong Tang
    • 1
  • Cuiping Zang
    • 1
  • Yong Wei
    • 1
  • Minghui Jiang
    • 1
  1. 1.Sichuan Engineering Technical CollegeDeyangChina

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