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Neural network modeling for groundwater-level forecasting in coastal aquifers

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Abstract

Advances in the artificial intelligence-based models can act as robust tools for modeling hydrological processes. Neural network architectures coupled with learning algorithms are considered as useful modeling tools for groundwater-level fluctuations. Emotional artificial neural network coupled with genetic algorithm (EANN-GA) is one such novel hybrid neural network which has been used in the present study for the forecasting of groundwater levels at three sites (Site H3, Site H4.5, and Site H9) in a coastal aquifer system. This study was conceived to address and investigate the efficiency of the ensemble model (EANN-GA) for forecasting one-month ahead groundwater level and to compare its performance with emotional artificial neural network (EANN), generalized regression neural network (GRNN), and the conventional feedforward neural network (FFNN). Variations in the rainfall, tidal levels, and groundwater levels are selected as inputs for the development of EANN-GA, EANN, GRNN, and FFNN models. Suitable goodness-of-fit criteria such as Nash–Sutcliffe efficiency (NSE), bias, root mean squared error (RMSE), and graphical indicators are used for assessing the efficiency of the developed models. The improvement in the performance of the EANN-GA model over the developed EANN, GRNN, and FFNN models in terms of NSE is 0.81, 6.02, and 9.56% at Site H3; 4.35, 5.50, and 22.68% at Site H4.5; and 1.05, 7.18, and 21.75% at Site H9. Thus, it can be inferred that the EANN-GA model outperforms the developed EANN model, GRNN model, and FFNN model. Further, this paper examines the predictive capability of extreme events by the EANN-GA, EANN, GRNN, and FFNN models. The RMSE values of the EANN-GA model at all peak points are found as 0.27, 0.23, and 0.10 m at sites H3, H4.5, and H9, respectively, and the results indicate superior performance of EANN-GA model. To check the generalization ability of the developed EANN-GA models, they are validated with the data of another site (Site I2) located in the same coastal aquifer. Superior prediction capability and generalization ability make the EANN-GA model a better alternative for predicting groundwater levels. Overall, this study demonstrates the effectiveness of EANN-GA in modeling spatio-temporal fluctuations of groundwater levels. It is also concluded that the EANN-GA model yields remarkably better predictions of extreme events, and hence, it could be a promising technique for developing alarm systems for real-world water problems.

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References

  1. IWMI (2001) The strategic plan for IWMI 2000–2005. IWMI, Colombo

    Google Scholar 

  2. Ketabchi H, Mahmoodzadeh D, Ataie-Ashtiani B, Simmons CT (2016) Sea-level rise impacts on seawater intrusion in coastal aquifers: review and integration. J Hydrol 535:235–255. https://doi.org/10.1016/j.jhydrol.2016.01.083

    Article  Google Scholar 

  3. Mehdizadeh SS, Karamalipour SE, Asoodeh R (2017) Sea level rise effect on seawater intrusion into layered coastal aquifers (simulation using dispersive and sharp-interface approaches). Ocean Coast Manag 138:11–18. https://doi.org/10.1016/j.ocecoaman.2017.01.001

    Article  Google Scholar 

  4. Dieng NM, Orban P, Otten J et al (2017) Temporal changes in groundwater quality of the Saloum coastal aquifer. J Hydrol Reg Stud 9:163–182. https://doi.org/10.1016/j.ejrh.2016.12.082

    Article  Google Scholar 

  5. Molinari A, Chidichimo F, Straface S, Guadagnini A (2014) Assessment of natural background levels in potentially contaminated coastal aquifers. Sci Total Environ 476–477:38–48. https://doi.org/10.1016/j.scitotenv.2013.12.125

    Article  Google Scholar 

  6. Barlow PM, Reichard EG (2010) Saltwater intrusion in coastal regions of North America. Hydrogeol J 18:247–260. https://doi.org/10.1007/s10040-009-0514-3

    Article  Google Scholar 

  7. Kong J, Xin P, Hua G-F et al (2015) Effects of vadose zone on groundwater table fluctuations in unconfined aquifers. J Hydrol 528:397–407. https://doi.org/10.1016/j.jhydrol.2015.06.045

    Article  Google Scholar 

  8. Levanon E, Shalev E, Yechieli Y, Gvirtzman H (2016) Fluctuations of fresh-saline water interface and of water table induced by sea tides in unconfined aquifers. Adv Water Resour 96:34–42. https://doi.org/10.1016/j.advwatres.2016.06.013

    Article  Google Scholar 

  9. Li H, Jiao JJ, Luk M, Cheung K (2002) Tide-induced groundwater level fluctuation in coastal aquifers bounded by L-shaped coastlines. Water Resour Res 38:2002

    Google Scholar 

  10. Huang F-K, Chuang M-H, Wang GS, Yeh H-D (2015) Tide-induced groundwater level fluctuation in a U-shaped coastal aquifer. J Hydrol 530:291–305

    Article  Google Scholar 

  11. Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124

    Article  Google Scholar 

  12. Coulibaly P, Anctil F, Aravena R, Bobée B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37:885–896. https://doi.org/10.1029/2000WR900368

    Article  Google Scholar 

  13. Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309:229–240. https://doi.org/10.1016/j.jhydrol.2004.12.001

    Article  Google Scholar 

  14. Jha MK, Sahoo S (2015) Efficacy of neural network and genetic algorithm techniques in simulating spatio-temporal fluctuations of groundwater. Hydrol Process 29:671–691. https://doi.org/10.1002/hyp.10166

    Article  Google Scholar 

  15. Mohanty S, Jha MK, Kumar A, Panda DK (2013) Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi-Surua Inter-basin of Odisha, India. J Hydrol 495:38–51. https://doi.org/10.1016/j.jhydrol.2013.04.041

    Article  Google Scholar 

  16. Nourani V, Alami MT, Vousoughi FD (2015) Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling. J Hydrol 524:255–269. https://doi.org/10.1016/j.jhydrol.2015.02.048

    Article  Google Scholar 

  17. Nourani V, Kisi Ö, Komasi M (2011) Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. J Hydrol 402:41–59

    Article  Google Scholar 

  18. Nourani V, Mogaddam AA, Nadiri AO (2008) An ANN-based model for spatiotemporal groundwater level forecasting. Hydrol Process 22:5054–5066

    Article  Google Scholar 

  19. Sahoo S, Jha MK (2013) Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment. Hydrogeol J 21:1865–1887. https://doi.org/10.1007/s10040-013-1029-5

    Article  Google Scholar 

  20. Supreetha BS, Nayak PK, Shenoy NK (2015) Groundwater level prediction using hybrid artificial neural network with genetic algorithm. Int J Earth Sci Eng 8:2609–2615

    Google Scholar 

  21. Mei Y, Tan G, Liu Z (2017) An improved brain-inspired emotional learning algorithm for fast classification. Algorithms. https://doi.org/10.3390/a10020070

    Article  MATH  Google Scholar 

  22. Ni YQ, Li M (2016) Wind pressure data reconstruction using neural network techniques: a comparison between BPNN and GRNN. Measurement 88:468–476

    Article  Google Scholar 

  23. Luo Q, Wu J, Yang Y et al (2016) Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty. J Hydrol 534:352–363. https://doi.org/10.1016/j.jhydrol.2016.01.009

    Article  Google Scholar 

  24. Cigizoglu HK (2005) Application of generalized regression neural networks to intermittent flow forecasting and estimation. J Hydrol Eng 10:336–341

    Article  Google Scholar 

  25. Sun AY, Wang D, Xu X (2014) Monthly streamflow forecasting using Gaussian process regression. J Hydrol 511:72–81

    Article  Google Scholar 

  26. Roshni T, Jha MK, Deo RC, Vandana A (2019) Development and evaluation of hybrid artificial neural network architectures for modeling spatio-temporal groundwater fluctuations in a complex aquifer system. Water Resour Manag 33:1–17

    Article  Google Scholar 

  27. Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013

    Article  Google Scholar 

  28. Almasri MN, Kaluarachchi JJ (2005) Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. Environ Model Softw 20:851–871. https://doi.org/10.1016/j.envsoft.2004.05.001

    Article  Google Scholar 

  29. Ebrahimi H, Rajaee T (2017) Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Glob Planet Change 148:181–191. https://doi.org/10.1016/j.gloplacha.2016.11.014

    Article  Google Scholar 

  30. Nourani V, Ejlali RG, Alami MT (2011) Spatiotemporal groundwater level forecasting in coastal aquifers by hybrid artificial neural network-geostatistics model: a case study. Environ Eng Sci 28:217–228

    Article  Google Scholar 

  31. Wu J, Long J, Liu M (2015) Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing 148:136–142. https://doi.org/10.1016/j.neucom.2012.10.043

    Article  Google Scholar 

  32. Muralitharan K, Sakthivel R, Vishnuvarthan R (2018) Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing 273:199–208. https://doi.org/10.1016/j.neucom.2017.08.017

    Article  Google Scholar 

  33. Li H, Qin SJ, Tsotsis TT, Sahimi M (2012) Computer simulation of gas generation and transport in landfills: VI—dynamic updating of the model using the ensemble Kalman filter. Chem Eng Sci 74:69–78. https://doi.org/10.1016/j.ces.2012.01.054

    Article  Google Scholar 

  34. Li H, Sanchez R, Joe Qin S et al (2011) Computer simulation of gas generation and transport in landfills. V: use of artificial neural network and the genetic algorithm for short- and long-term forecasting and planning. Chem Eng Sci 66:2646–2659. https://doi.org/10.1016/j.ces.2011.03.013

    Article  Google Scholar 

  35. Li H, Tsotsis TT, Sahimi M, Qin SJ (2014) Ensembles-based and GA-based optimization for landfill gas production. AIChE J 60:2063–2071. https://doi.org/10.1002/aic.14396

    Article  Google Scholar 

  36. Thenius R, Zahadat P, Schmickl T (2013) EMANN—a model of emotions in an artificial neural network. In: ECAL twelfth European conference on artificial, immune, neural and endocrine systems, pp 830–837

  37. Lotfi E, Khosravi A, Nahavandi S (2014) Wind power forecasting using emotional neural networks. In: 2014 IEEE international conference on systems, man, and cybernetics, pp 311–316

  38. Lotfi E, Akbarzadeh MR (2014) Practical emotional neural networks. Neural Netw 59:61–72. https://doi.org/10.1016/j.neunet.2014.06.012

    Article  Google Scholar 

  39. Nourani V (2017) An emotional ANN (EANN) approach to modeling rainfall–runoff process. J Hydrol 544:267–277. https://doi.org/10.1016/j.jhydrol.2016.11.033

    Article  Google Scholar 

  40. Moren J (2002) Emotion and learning—a computational model of the amygdala. Lund University Cognitive Science, Lund

    Google Scholar 

  41. Lotfi E, Akbarzadeh MR (2016) A winner-take-all approach to emotional neural networks with universal approximation property. Inf Sci (NY) 346–347:369–388. https://doi.org/10.1016/j.ins.2016.01.055

    Article  Google Scholar 

  42. Babaie T, Karimizandi R, Lucas C (2008) Learning based brain emotional intelligence as a new aspect for development of an alarm system. Soft Comput 12:857–873. https://doi.org/10.1007/s00500-007-0258-8

    Article  Google Scholar 

  43. Khashman A (2009) Application of an emotional neural network to facial recognition. Neural Comput Appl 18:309–320

    Article  Google Scholar 

  44. Khashman A (2009) Blood cell identification using emotional neural networks. J Inf Sci Eng 25:1737–1751

    Google Scholar 

  45. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576

    Article  Google Scholar 

  46. Luo X, Yuan X, Zhu S et al (2019) A hybrid support vector regression framework for streamflow forecast. J Hydrol 568:184–193

    Article  Google Scholar 

  47. Bendu H, Deepak B, Murugan S (2017) Multi-objective optimization of ethanol fuelled HCCI engine performance using hybrid GRNN–PSO. Appl Energy 187:601–611

    Article  Google Scholar 

  48. Fellous J-M (1999) Neuromodulatory basis of emotion. The Neuroscientist 5:283–294

    Article  Google Scholar 

  49. Jha MK, Chikamori K, Kamii Y, Yamasaki Y (1999) Field investigations for sustainable groundwater utilization in the Konan basin. Water Resour Manag 13(6):443–470. https://doi.org/10.1023/A:1008184010262

    Article  Google Scholar 

  50. Kemp SE, Wilson ID, Ware JA (2004) A tutorial on the gamma test. Int J Simul Syst Sci Technol 6:67–75

    Google Scholar 

  51. Roshni T, Kumari N, Renji R, Drisya J (2017) Modelling land surface temperature using gamma test coupled wavelet neural network. Adv Environ Res 6(4):265–279

    Google Scholar 

  52. Evans D, Jones AJ (2002) A proof of the gamma test. Proc R Soc Lond Ser A Math Phys Eng Sci 458:2759–2799

    Article  MathSciNet  Google Scholar 

  53. R Studio (2017) R Studio: integrated development environment for R. R version. Newnes, Boston

    Google Scholar 

  54. Haykin SS, Haykin SS, Haykin SS, Haykin SS (2009) Neural networks and learning machines. Pearson, Upper Saddle River, NJ

    MATH  Google Scholar 

  55. Roshni T, Md. Sajid K, Samui P (2017) Potential of regression models in projecting sea level variability due to climate change at Haldia Port, India. Ocean Syst Eng 7:319–328. https://doi.org/10.12989/ose.2017.7.4.319

    Article  Google Scholar 

  56. DelSole T, Tippett MK (2016) Forecast comparison based on random walks. Mon Weather Rev 144:615–626

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Dr. Ehsan Lotfi, Department of Computer Engineering, Torbat-e-Jam Branch, Islamic Azad University, Torbat-e-Jam, Iran, for sharing the toolbox for the EANN and EANN-GA models and also for his help in technical discussion about the EANN model development. We also sincerely thank the three anonymous reviewers and the Editor/Associate Editor for their thoughtful comments/suggestions that improved the overall quality of this paper.

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology, Patna, India

    Thendiyath Roshni

  2. AgFE Department, Indian Institute of Technology Kharagpur, Kharagpur, India

    Madan K. Jha

  3. Department of Civil Engineering, National Institute of Technology, Calicut, India

    J. Drisya

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  1. Thendiyath Roshni
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Correspondence to Thendiyath Roshni or Madan K. Jha.

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Appendix: Model architectures developed

Appendix: Model architectures developed

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Roshni, T., Jha, M.K. & Drisya, J. Neural network modeling for groundwater-level forecasting in coastal aquifers. Neural Comput & Applic 32, 12737–12754 (2020). https://doi.org/10.1007/s00521-020-04722-z

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