202
Views
4
CrossRef citations to date
0
Altmetric
Research Article

Assessment of wavelet-SVR and wavelet-GP models in predicting the groundwater level using areal precipitation and consumption data

ORCID Icon & ORCID Icon
Pages 1026-1039 | Received 16 Sep 2021, Accepted 07 Mar 2022, Published online: 10 May 2022

References

  • Adamowski, J. and Chan, H.F., 2011. A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407 (1–4), 28–40. doi:10.1016/j.jhydrol.2011.06.013
  • Awad, M. and Khanna, R., 2015. Support vector regression. In: Efficient learning machines: theories, concepts, and applications for engineers and system designers. Berkeley, CA: Apress, 67–80. doi:10.1007/978-1-4302-5990-9_4
  • Cannas, B., et al., 2006. Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Physics and Chemistry of the Earth, Parts A/B/C, 31 (18), 1164–1171. doi:10.1016/j.pce.2006.03.020
  • Cherkassky, V. and Ma, Y., 2004. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks, 17 (1), 113–126. doi:10.1016/S0893-6080(03)00169-2
  • Cramer, N.L., 1985. A representation for the adaptive generation of simple sequential programs. ed. In: Proceedings of an international conference on genetic algorithms and the applications, Dallas, TX, 183–187.
  • Daubechies, I., 1990. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 36 (5), 961–1005. doi:10.1109/18.57199
  • Derakhti, K. and Alaf-Najib, M., 2005. Explanatory report for the extension of the prohobitation on Ajabshir plain. Regional Water Company of Esat Azerbaijan Province, Report in Persian, 1009.
  • Doroudi, S., Sharafati, A., and Mohajeri, S.H., 2021. Estimation of daily suspended sediment load using a novel hybrid support vector regression model incorporated with observer-teacher-learner-based optimization method. Complexity, 2021, 1–13. doi:10.1155/2021/5540284
  • Drago, A. and Boxall, S., 2002. Use of the wavelet transform on hydro-meteorological data. Physics and Chemistry of the Earth, Parts A/B/C, 27 (32–34), 1387–1399. doi:10.1016/S1474-7065(02)00076-1
  • Ebrahimi, H. and Rajaee, T., 2017. Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global and Planetary Change, 148, 181–191. doi:10.1016/j.gloplacha.2016.11.014
  • Fallah-Mehdipour, E., Haddad, O.B., and Mariño, M., 2013. Prediction and simulation of monthly groundwater levels by genetic programming. Journal of Hydro-Environment Research, 7 (4), 253–260. doi:10.1016/j.jher.2013.03.005
  • Famiglietti, J.S., 2014. The global groundwater crisis. Nature Climate Change, 4 (11), 945–948. doi:10.1038/nclimate2425
  • Gürsoy, Ö. and Engin, S.N., 2019. A wavelet neural network approach to predict daily river discharge using meteorological data. Measurement and Control, 52 (5–6), 599–607. doi:10.1177/0020294019827972
  • Guzman, S.M., et al., 2019. Evaluation of seasonally classified inputs for the prediction of daily groundwater levels: NARX networks vs support vector machines. Environmental Modeling & Assessment, 24 (2), 223–234. doi:10.1007/s10666-018-9639-x
  • Haghbin, M., et al., 2021. Application of soft computing models for simulating nitrate contamination in groundwater: comprehensive review, assessment and future opportunities. Archives of Computational Methods in Engineering, 28 (5), 3569–3591. doi:10.1007/s11831-020-09513-2
  • Idrizovic, D., et al., 2020. Impact of climate change on water resource availability in a mountainous catchment: a case study of the Toplica River catchment, Serbia. Journal of Hydrology, 587, 124992. doi:10.1016/j.jhydrol.2020.124992
  • Iqbal, N., et al., 2021. Groundwater level prediction model using correlation and difference mechanisms based on boreholes data for sustainable hydraulic resource management. IEEE Access, 9, 96092–96113. doi:10.1109/ACCESS.2021.3094735
  • Jafari, M.M., et al., 2021. Application of a novel hybrid wavelet-ANFIS/fuzzy c-means clustering model to predict groundwater fluctuations. Atmosphere, 12 (1), 9. doi:10.3390/atmos12010009
  • Kasiviswanathan, K., et al., 2016. Genetic programming based monthly groundwater level forecast models with uncertainty quantification. Modeling Earth Systems and Environment, 2 (1), 27. doi:10.1007/s40808-016-0083-0
  • Ke, Q., et al., 2020. Urban pluvial flooding prediction by machine learning approaches–a case study of Shenzhen city, China. Advances in Water Resources, 145, 103719. doi:10.1016/j.advwatres.2020.103719
  • Koza, J.R., 1992. Genetic programming: on the programming of computers by means of natural selection. Cambridge, MA: MIT press.
  • Kuczera, G., 1992. Uncorrelated measurement error in flood frequency inference. Water Resources Research, 28 (1), 183–188. doi:10.1029/91WR02269
  • Liong, S.-Y., et al., 2002. Genetic programming: a new paradigm in rainfall runoff modeling. JAWRA Journal of the American Water Resources Association, 38 (3), 705–718. doi:10.1111/j.1752-1688.2002.tb00991.x
  • Lopes, H.S. and Weinert, W.R., 2004. EGIPSYS: an enhanced gene expression programming approach for symbolic regression problems. International Journal of Applied Mathematics and Computer Science, 14 (3), 375–384.
  • Ma, J.-Z., Lai, T.-W., and Li, -J.-J., 2002. The impact of human activities on groundwater resources in the south edge of Tarim Basin, Xinjiang. Chinese Geographical Science, 12 (1), 50–54. doi:10.1007/s11769-002-0070-4
  • Mallat, S.G., 1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11 (7), 674–693. doi:10.1109/34.192463
  • Mirabbasi, R., Sattari, M., and Velinjagh, V., 2016. Simulation and groundwater utilization management of Ajabshir plain aquifer (In Farsi). Hydrogeology, 1, 54–75.
  • Mohammadi, K., 2009. Groundwater table estimation using MODFLOW and artificial neural networks. In: Practical hydroinformatics. Springer, 127–138. doi:10.1007/978-3-540-79881-1_10
  • Moosavi, V., et al., 2013. A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resources Management, 27 (5), 1301–1321. doi:10.1007/s11269-012-0239-2
  • Nie, S., et al., 2017. Simulation and uncertainty analysis for groundwater levels using radial basis function neural network and support vector machine models. Journal of Water Supply: Research and Technology—AQUA, 66 (1), 15–24. doi:10.2166/aqua.2016.069
  • Partal, T. and Kişi, Ö., 2007. Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology, 342 (1–2), 199–212. doi:10.1016/j.jhydrol.2007.05.026
  • Platt, J., 1999. Sequential minimal optimization: a fast algorithm for training support vector machines. Advances in Kernel methods-support vector learning. Cambridge, MA: MIT Press, 185–208.
  • Prinos, S.T., Lietz, A., and Irvin, R., 2002. Design of a real-time ground-water level monitoring network and portrayal of hydrologic data in southern Florida. US Geological Survey. doi:10.3133/wri20014275
  • Raghavendra, N.S. and Deka, P.C., 2015. Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid wavelet packet–support vector regression. Cogent Engineering, 2 (1), 999414. doi:10.1080/23311916.2014.999414
  • Ramana, R.V., et al., 2013. Monthly rainfall prediction using wavelet neural network analysis. Water Resources Management, 27 (10), 3697–3711. doi:10.1007/s11269-013-0374-4
  • Sahoo, S. and Jha, M.K., 2013. Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment. Hydrogeology Journal, 21 (8), 1865–1887. doi:10.1007/s10040-013-1029-5
  • Sang, Y.-F., et al., 2009. The relation between periods’ identification and noises in hydrologic series data. Journal of Hydrology, 368 (1–4), 165–177. doi:10.1016/j.jhydrol.2009.01.042
  • Shafaei, M. and Kisi, O., 2016. Lake level forecasting using wavelet-SVR, wavelet-ANFIS and wavelet-ARMA conjunction models. Water Resources Management, 30 (1), 79–97. doi:10.1007/s11269-015-1147-z
  • Sharafati, A., Asadollah, S.B.H.S., and Neshat, A., 2020. A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. Journal of Hydrology, 591, 125468. doi:10.1016/j.jhydrol.2020.125468
  • Sharafati, A., Asadollah, S.B.H.S., and Shahbazi, A., 2021. Assessing the impact of climate change on urban water demand and related uncertainties: a case study of Neyshabur, Iran. Theoretical and Applied Climatology, 1–15. doi:10.1007/s00704-021-03638-5
  • Sharafati, A., Yasa, R., and Azamathulla, H.M., 2018. Assessment of stochastic approaches in prediction of wave-induced pipeline scour depth. Journal of Pipeline Systems Engineering and Practice, 9 (4), 04018024. doi:10.1061/(ASCE)PS.1949-1204.0000347
  • Singh, C.K. and Katpatal, Y.B., 2020. Assessment of groundwater-level monitoring network in irrigated regions with a complex aquifer system using information theory. Journal of Hydrologic Engineering, 25 (11), 05020040. doi:10.1061/(ASCE)HE.1943-5584.0002004
  • Sivapragasam, C., et al., 2015. Assessing suitability of GP modeling for groundwater level. Aquatic Procedia, 4, 693–699. doi:10.1016/j.aqpro.2015.02.089
  • Sreekanth, P., et al., 2011. Comparison of FFNN and ANFIS models for estimating groundwater level. Environmental Earth Sciences, 62 (6), 1301–1310. doi:10.1007/s12665-010-0617-0
  • Suryanarayana, C., et al., 2014. An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing, 145, 324–335. doi:10.1016/j.neucom.2014.05.026
  • Torabi Haghighi, A., et al., 2020. Unsustainability syndrome—from meteorological to agricultural drought in arid and semi-arid regions. Water, 12 (3), 838. doi:10.3390/w12030838
  • Vapnik, V., 1998. Statistical learning theory New York. New York: Wiley, 443–454.
  • Wang, W. and Ding, J., 2003. Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1 (1), 67–71. Available from: http://www.paper.edu.cn/scholar/showpdf/NUT2UN1IMTj0AxeQh
  • Wei, S., Song, J., and Khan, N.I., 2012. Simulating and predicting river discharge time series using a wavelet‐neural network hybrid modelling approach. Hydrological Processes, 26 (2), 281–296. doi:10.1002/hyp.8227
  • Wen, X., et al., 2015. Wavelet and adaptive neuro-fuzzy inference system conjunction model for groundwater level predicting in a coastal aquifer. Neural Computing and Applications, 26 (5), 1203–1215. doi:10.1007/s00521-014-1794-7
  • Yoon, H., et al., 2016. A method to improve the stability and accuracy of ANN-and SVM-based time series models for long-term groundwater level predictions. Computers & Geosciences, 90, 144–155. doi:10.1016/j.cageo.2016.03.002
  • Yu, H., et al., 2018. Comparative study of hybrid-wavelet artificial intelligence models for monthly groundwater depth forecasting in extreme arid regions, Northwest China. Water Resources Management, 32 (1), 301–323. doi:10.1007/s11269-017-1811-6

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.