Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Application of Several Data-Driven Techniques for Predicting Groundwater Level

Abstract

In this study, several data-driven techniques including system identification, time series, and adaptive neuro-fuzzy inference system (ANFIS) models were applied to predict groundwater level for different forecasting period. The results showed that ANFIS models out-perform both time series and system identification models. ANFIS model in which preprocessed data using fuzzy interface system is used as input for artificial neural network (ANN) can cope with non-linear nature of time series so it can perform better than others. It was also demonstrated that all above mentioned approaches could model groundwater level for 1 and 2 months ahead appropriately but for 3 months ahead the performance of the models was not satisfactory.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Ahn H (2000) Modeling of groundwater heads based on second-order difference time series models. J Hydrol 234(1–2):82–94

  2. Akaike H (1974) A new look at the statistical model identification. Autom Control IEEE Trans 19(6):716–723. doi:10.1109/tac.1974.1100705

  3. Altunkaynak A (2007) Forecasting surface water level fluctuations of lake Van by artificial neural networks. Water Resour Manage 21(2):399–408. doi:10.1007/s11269-006-9022-6

  4. Amabile V, Gabriel G, Bernard AE (2008) Fitting of time series models to forecast streamflow and groundwater using simulated data from SWAT. J Hydrol Eng 13(7):554–562

  5. Box GEP, Jenkins GM, Reinsel GC (2008) Time Series Analysis: Forecasting and Control. Holden Day

  6. Celik O, Ertugrul S (2010) Predictive human operator model to be utilized as a controller using linear, neuro-fuzzy and fuzzy-ARX modeling techniques. Eng Appl Artif Intell 23(4):595–603. doi:10.1016/j.engappai.2009.08.007

  7. Chang FJ, Chang YT (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29:1–10

  8. Chu H-J, Chang L-C (2009) Application of optimal control and fuzzy theory for dynamic groundwater remediation design. Water Resour Manage 23(4):647–660. doi:10.1007/s11269-008-9293-1

  9. Dalcin C, Moens WL, Dierickx PH, Bastin G, Zech Y (2005) An integrated approach for real time flood map forecasting on the Belgian Meuse River. Nat Hazard 36:237–256

  10. Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309(1–4):229–240

  11. Erdem E, Shi J (2011) ARMA based approaches for forecasting the tuple of wind speed and direction. Appl Energy 88(4):1405–1414

  12. Erdoğan H, Gulal E (2009) Identification of dynamic systems using Multiple Input–Single Output (MISO) models. Nonlinear Anal: Real World Appl 10(2):1183–1196

  13. Faruk D (2010) A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artif Intel 23(4):586–594

  14. Firat M, Turan ME, Yurdusev MA (2009) Comparative analysis of fuzzy inference systems for water consumption time series prediction. J Hydrol 374(3–4):235–241

  15. French MN, Krajewski WF, Cuykendall RR (1992) Rainfall forecasting in space and time using a neural network. J Hydrol (137):1–31

  16. Grimes DIF, Coppola E, Verdecchia M, Visconti G (2003) A neural network approach to real time rainfall estimation for Africa using Satellite data. J Hydro Meteorol (4): 1119–1133

  17. Han P, Wang PX, Zang SY, De Hai Z (2010) Drought forecasting based on the remote sensing data using ARIMA models. Math Comput Model 51:1398–1403

  18. Hasebe M, Nagayama Y (2002) Reservoir operation using the neural Network and fuzzy systems for dam control and operation support. Adv Eng Softw 33(5):245–260

  19. Hasmida H (2009) Water quality trend at the upper part of johor river in relation to rainfall and runoff pattern. MS thesis, Faculty of Civil Engineeing, Universitiy Teknologi, Malaysia

  20. Irvine KN, Eberhardt AJ (1992) Multiplicative, seasonal ARIMA models for Lake Erieand Lake Ontario water levels. JAWRA J Am Water Resour Assoc 28(2):385–396. doi:10.1111/j.1752-1688.1992.tb04004.x

  21. Jain SK, Das A, Srivastava DK (1999) Application of ANN for Reservoir in flow prediction and operation. J Water Resour Plan Manage 125(5):263–271

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

  23. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Prentice-Hall, Eaglewood cliffs. doi:10.1109/tac.1997.633847

  24. Keskin ME, Terzi Ö, Taylan D (2004) Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey/Estimation de l’évaporation journalière du bac dans l’Ouest de la Turquie par des modèles à base de logique floue. Hydrol Sci J 49(6):1001–1010. doi:10.1623/hysj.49.6.1001.55718

  25. Kisi O (2009) Neural networks and wavelet conjunction model for intermittent streamflow forecasting. J Hydrol Eng 14(8):773–782

  26. Kisi O (2010) Wavelet regression model for short-term streamflow forecasting. J Hydrol 389(3–4):344–353

  27. Konikow LF, Kendy E (2005) Groundwater depletion: A global problem. Hydrogeol J 13(1):317–320. doi:10.1007/s10040-004-0411-8

  28. Kosko B (1993) Fuzzy Thinking: The New Science of Fuzzy Logic. Flamingo

  29. Ljung L (1995) System identification toolbox. MathWorks, Inc. pp:274

  30. Luk KC, Ball JE, Sharma A (2000) A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J Hydrol 227(1–4):56–65

  31. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1–2):52–66

  32. Park YS, Rabinovich J, Lek S (2007) Sensitivity analysis and stability patterns of two-species pest models using artificial neural networks. Ecol Model 204:427–438

  33. Pekarova P, Onderka M, Pekar J, Roncak P, Miklane KP (2009) Prediction of water quality in the Danube River under extreme hydrological and temperature condition. J Hydrol Hydromechanics 57(1):3–15

  34. Rajaee T (2011) Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. Sci Total Environ 409(15):2917–2928

  35. Raman H, Chandramouli V (1996) Deriving a general operating policy for reservoirs using neural network. J Water Resour Plan Manage 122(5):342–347

  36. Ross TJ (1995) Fuzzy Logic with Engineering Applications. Willy

  37. Russel SO, Campbell PF (1996) Reservoir operating rules with fuzzy programming. J Water Resour Plan Manag ASCE 122(3):165–170

  38. Şen Z, Kadioğlu M, Batur E (2000) Stochastic modeling of the Van Lake monthly level fluctuations in Turkey. Theor Appl Climatol 65(1):99–110. doi:10.1007/s007040050007

  39. Shu LC, Wang MM, Liu RG, Chen GH (2007) Sensitivity analysis of parameters in numerical simulation of groundwater. J Hohai Univ (Nat Sci) 35(5):491–495

  40. Talebizadeh M, Moridnejad A (2011) Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Syst Appl 38(4):4126–4135

  41. Talei A, Chua LHC, Wong TSW (2010) Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference System (ANFIS) in rainfall–runoff modeling. J Hydrol 391:248–262

  42. Tokar AS, Johnson PA (1999) Rainfall-runoff modeling using artificial neural networks. J Hydrol Eng ASCE 4(3):232–239

  43. Vafakhah M (2012) Application of artificial neural networks and adaptive neuro-fuzzy inference system models to short-term streamflow forecasting. Can J Civ Eng 39(4):402–414. doi:10.1139/l2012-011

  44. Vaziri M (1997) Predicting Caspian Sea surface water level by ANN and ARIMA models. ASCE J Waterw Port Coast Ocean Eng 123(4):158–162

  45. Wong H, W-c I, Zhang R, Xia J (2007) Non-parametric time series models for hydrological forecasting. J Hydrol 332(3–4):337–347

  46. Yarar A, Onucyıldız M, Copty NK (2009) Modelling level change in lakes using neuro-fuzzy and artificial neural networks. J Hydrol 365(3–4):329–334

  47. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

Download references

Author information

Correspondence to Mehdi Vafakhah.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Shirmohammadi, B., Vafakhah, M., Moosavi, V. et al. Application of Several Data-Driven Techniques for Predicting Groundwater Level. Water Resour Manage 27, 419–432 (2013). https://doi.org/10.1007/s11269-012-0194-y

Download citation

Keywords

  • Groundwater level prediction
  • System identification
  • Time series
  • ANFIS