Enhanced Data Utilization Approach to Improve the Prediction Performance of Groundwater Level Using Semianalytical and Data Process Models
Publication: Journal of Hydrologic Engineering
Volume 27, Issue 10
Abstract
Methods for the estimation of groundwater level (GWL) are often based on knowledge- and data-based approaches, which are largely affected by the representativeness of input parameters and the quantity and quality of previously collected data sets, respectively. In this study, a new hybrid GWL prediction model combining knowledge- and data-based approaches is proposed using a signal decomposing technique to improve the efficiency and accuracy of GWL prediction with less data dependency. The target site condition was urban areas near a river, where the river stage is the dominant influence on GWL. For this purpose, the engineering groundwater-prediction model (EGPM) as a knowledge-based method and multiple linear regression, artificial neural network (ANN), and wavelet ANN (WANN) as data-based methods were employed and adopted to establish the proposed hybrid GWL prediction model. Case studies in the Korean and Japanese contexts were performed to compare and assess results predicted by existing methods and the proposed hybrid method. It was shown that the proposed hybrid method can enhance the GWL prediction performance with improved accuracy of the prediction and efficiency of database utilization. It was also indicated that the required data length can be alleviated while producing a satisfactory prediction throughout the frequency range of GWL fluctuations.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Basic Science Research Program through the Korea Institute of Energy Technology Evaluation and Planning (KETEP), the Ministry of Trade, Industry & Energy (MOTIE), the National Research Foundation of Korea (NRF), and the Korea Agency for Infrastructure Technology Advancement (KAIA) with grants funded by the government of Korea (Nos. 20194030202460, 2020R1A2C2011966, and 20SMIP-A158708-01).
References
Adamowski, J., and H. F. Chan. 2011. “A wavelet neural network conjunction model for groundwater level forecasting.” J. Hydrol. 407 (1–4): 28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013.
ALOS (Advanced Land Observing Satellite). 2017. “High-resolution land-use land cover map.” Accessed July 2, 2017. https://www.eorc.jaxa.jp/ALOS/jp/index_j.htm.
Anctil, F., C. Perrin, and V. Andreassian. 2004. “Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models.” Environ. Modell. Software 19 (4): 357–368. https://doi.org/10.1016/S1364-8152(03)00135-X.
Ausilio, E., and E. Conte. 2005. “Influence of groundwater on the bearing capacity of shallow foundations.” Can. Geotech. J. 42 (2): 663–672. https://doi.org/10.1139/t04-084.
Bansal, R. K., and S. K. Das. 2010. “An analytical study of water table fluctuations in unconfined aquifers due to varying bed slopes and spatial location of the recharge basin.” J. Hydrol. Eng. 15 (11): 909–917. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000267.
Bowes, B. D., J. M. Sadler, M. M. Morsy, M. Behl, and J. L. Goodall. 2019. “Forecasting groundwater table in a flood prone coastal city with long short-term memory and recurrent neural networks.” Water 11 (5): 1098. https://doi.org/10.3390/w11051098.
Chesnaux, R., R. P. Chapuis, and J. W. Molson. 2006. “A new method to characterize hydraulic short-circuits in defective borehole seals.” Groundwater 44 (5): 676–681. https://doi.org/10.1111/j.1745-6584.2006.00208.x.
Choubin, B., and A. Malekian. 2017. “Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions.” Environ. Earth Sci. 76 (15): 538. https://doi.org/10.1007/s12665-017-6870-8.
Christopoulou, E. B., A. N. Skodras, and A. A. Georgakilas. 2002. “The ‘Trous’ wavelet transform versus classical methods for the improvement of solar images.” In Vol. 2 of Proc., 14th Int. Conf. on Digit Signal Process, 885–888. https://doi.org/10.1109/ICDSP.2002.1028232.
Coppola, E., F. Szidarovszky, M. Poulton, and E. Charles. 2003. “Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions.” J. Hydrol. Eng. 8 (6): 348–359. https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(348).
Coppola, E. A., A. J. Rana, M. M. Poulton, F. Szidarovszky, and V. W. Uhl. 2005. “A neural network model for predicting aquifer water level elevations.” Groundwater 43 (2): 231–241. https://doi.org/10.1111/j.1745-6584.2005.0003.x.
Coulibaly, P., F. Anctil, R. Aravena, and B. Bobee. 2001. “Artificial neural network modeling of water table depth fluctuations.” Water Resour. Res. 37 (4): 885–896. https://doi.org/10.1029/2000WR900368.
Daliakopoulos, I. N., P. Coulibaly, and I. K. Tsanis. 2005. “Groundwater level forecasting using artificial neural networks.” J. Hydrol. 309 (1–4): 229–240. https://doi.org/10.1016/j.jhydrol.2004.12.001.
Dawson, C. W., and R. L. Wilby. 2001. “Hydrological modelling using artificial neural networks.” Prog. Phys. Geogr. 25 (1): 80–108. https://doi.org/10.1177/030913330102500104.
Dekker, S. C., W. Bouten, and M. G. Schaap. 2001. “Analysing forest transpiration model errors with artificial neural networks.” J. Hydrol. 246 (1–4): 197–208. https://doi.org/10.1016/S0022-1694(01)00368-7.
EGIS (Environmental Geographic Information Service). 2017. “Land cover map.” Accessed June 28, 2017. https://egis.me.go.kr.
Foresti, L., M. Reyniers, A. Seed, and L. Delobbe. 2016. “Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium.” Hydrol. Earth Syst. Sci. 20 (1): 505–527. https://doi.org/10.5194/hess-20-505-2016.
Gerken, W. C., L. K. Purvis, and R. J. Butera. 2006. “Genetic algorithm for optimization and specification of a neuron model.” Neuroconputing 69 (10–12): 1039–1042. https://doi.org/10.1016/j.neucom.2005.12.041.
Grossman, A., and J. Morlet. 1984. “Decomposition of hardy functions into square integrable wavelets of constant shape.” J. Math. Anal. Appl. 15 (Jul): 723–736. https://doi.org/10.1137/0515056.
Guzmán, P., C. Anibas, O. Batelaan, M. Huysmans, and G. Wyseure. 2016. “Hydrological connectivity of alluvial Andean valleys: A groundwater/surface-water interaction case study in Ecuador.” Hydrogeol. J. 24 (4): 955–969. https://doi.org/10.1007/s10040-015-1361-z.
Izady, A., K. Davary, A. Alizadeh, B. Ghahraman, M. Sadeghi, and A. Moghaddamnia. 2012. “Application of ‘panel-data’ modeling to predict groundwater levels in the Neishaboor Plain, Iran.” Hydrogeol. J. 20 (3): 435–447. https://doi.org/10.1007/s10040-011-0814-2.
Jha, M. K., and V. M. Chowdary. 2007. “Challenges of using remote sensing and GIS in developing nations.” Hydrogeol. J. 15 (1): 197–200. https://doi.org/10.1007/s10040-006-0117-1.
JMA (Japan Meteorological Agency). 2017. “Data and materials.” [In Japanese.] Accessed July 2, 2017. https://www.jma.go.jp.
Jones, A. J. 1993. “Genetic algorithms and their applications to the design of neural networks.” Neural Comput. Appl. 1 (Mar): 32–45. https://doi.org/10.1007/BF01411373.
Kajewska-Szkudlarek, J., and W. Łyczko. 2021. “Assessment of Hellwig method for predictors’ selection in groundwater level time series forecasting.” Water 13 (6): 778. https://doi.org/10.3390/w13060778.
Kayhomayoon, Z., S. G. Milan, N. A. Azar, and H. K. Moghaddam. 2021. “A new approach for regional groundwater level simulation: Clustering, simulation, and optimization.” Nat. Resour. Res. 30 (6): 4165–4185. https://doi.org/10.1007/s11053-021-09913-6.
Kim, I., and J. Lee. 2018. “Prediction model for spatial and temporal variation of groundwater level based on river stage.” J. Hydrol. Eng. 23 (6): 06018002. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001658.
Kim, I., and J. Lee. 2019. “Proposed correlation model for groundwater level prediction based on river stage considering changes in hydrological and geological conditions.” J. Hydrol. Eng. 24 (10): 04019042. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001849.
Kim, I., and J. Lee. 2022. “Performance analysis of ANN prediction for groundwater level considering regional-specific influence components.” Groundwater 60 (3): 344–361. https://doi.org/10.1111/gwat.13156.
KMA (Korea Meteorological Administration). 2017. “Observation, climate.” [In Korean.] Accessed June 28, 2017. https://www.kma.go.kr.
Kusaka, T., S. Sreng, R. Uzuoka, T. Ito, and A. Mochizuki. 2011. “Study on ground upheaval caused by the rise in groundwater level by centrifuge tests and by numerical simulations.” [In Japanese.] Jpn. Geotech. J. 6 (3): 439–454. https://doi.org/10.3208/jgs.6.439.
Lafare, A. E. A., D. W. Peach, and A. G. Hughes. 2016. “Use of seasonal trend decomposition to understand groundwater behaviour in the Permo-Triassic Sandstone aquifer, Eden Valley, UK.” Hydrogeol. J. 24 (1): 141–158. https://doi.org/10.1007/s10040-015-1309-3.
Laux, P., S. Vogl, W. Qiu, H. Knoche, and H. Kunstmann. 2011. “Copula-based statistical refinement of precipitation in RCM simulations over complex terrain.” Hydrol. Earth Syst. Sci. 15 (7): 2401–2419. https://doi.org/10.5194/hess-15-2401-2011.
Levanon, E., Y. Yechieli, H. Gvirtzman, and E. Shalev. 2017. “Tide-induced fluctuations of salinity and groundwater level in unconfined aquifers–Field measurements and numerical model.” J. Hydrol. 551 (Aug): 665–675. https://doi.org/10.1016/j.jhydrol.2016.12.045.
Makridakis, S., S. C. Wheelwright, and R. J. Hyndman. 2008. Forecasting methods and applications. 3rd ed. Singapore: Wiley.
MLIT (Ministry of Land, Infrastructure, Transport and Tourism). 2017. “Water information system page.” Accessed June 3, 2017. www1.river.go.jp.
Moosavi, V., M. Vafakhah, B. Shirmohammadi, and N. Behnia. 2013. “A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods.” Water Resour. Manage. 27 (5): 1301–1321. https://doi.org/10.1007/s11269-012-0239-2.
Nikolos, I. K., M. Stergiadi, M. P. Papadopoulou, and G. P. Karatzas. 2008. “Artificial neural networks as an alternative approach to groundwater numerical modeling and environmental design.” Hydrol. Processes 22 (17): 3337–3348. https://doi.org/10.1002/hyp.6916.
Nourani, V., A. A. Mogaddam, and A. O. Nadiri. 2008. “An ANN-based model for spatiotemporal groundwater level forecasting.” Hydrol. Processes 22 (26): 5054–5066. https://doi.org/10.1002/hyp.7129.
Nourani, V., and S. Mousavi. 2016. “Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method.” J. Hydrol. 536 (May): 10–25. https://doi.org/10.1016/j.jhydrol.2016.02.030.
Oh, Y.-Y., S.-Y. Hamm, H. Yoon, and G.-B. Kim. 2016. “Analytical and statistical approach for evaluating the effects of a river barrage on river–aquifer interactions.” Hydrol. Processes 30 (21): 3932–3948. https://doi.org/10.1002/hyp.10920.
Park, D., I. Kim, G. Kim, and J. Lee. 2017. “Groundwater effect factors for the load-carrying behavior of footings from hydraulic chamber load tests.” Geotech. Test. J. 30 (3): 440–451. https://doi.org/10.1520/GTJ20160078.
Park, D., I. Kim, G. Kim, and J. Lee. 2019. “Effect of groundwater fluctuation on load carrying performance of shallow foundation.” Geomech. Eng. 18 (6): 575–584. https://doi.org/10.12989/gae.2019.18.6.575.
Pauwels, V. R. N., N. E. C. Verhoest, and F. P. De Troch. 2002. “A metahillslope model based on an analytical solution to a linearized Boussinesq equation for temporally variable recharge rates.” Water Resour. Res. 38 (12): 1–14. https://doi.org/10.1029/2001WR000714.
Rajaee, T., H. Ebrahimi, and V. Nourani. 2019. “A review of the artificial intelligence methods in groundwater level modeling.” J. Hydrol. 572 (May): 336–351. https://doi.org/10.1016/j.jhydrol.2018.12.037.
Rezaie-balf, M., S. R. Naganna, A. Ghaemi, and P. C. Deka. 2017. “Wavelet coupled MARS and M5 model tree approaches for groundwater level forecasting.” J. Hydrol. 553 (Oct): 356–373. https://doi.org/10.1016/j.jhydrol.2017.08.006.
Rodrigues, R. A., F. V. Prado Soares, and M. Sanchez. 2021. “Settlement of footings on compacted and natural collapsible soils upon loading and soaking.” J. Geotech. Geoenviron. Eng. 147 (4): 04021010. https://doi.org/10.1061/(ASCE)GT.1943-5606.0002479.
Rosat, S., J.-P. Boy, G. Ferhat, J. Hinderer, M. Amalvict, P. Gegout, and B. Luck. 2009. “Analysis of a 10-year (1997–2007) record of time-varying gravity in Strasbourg using absolute and superconducting gravimeters: New results on the calibration and comparison with GPS height changes and hydrology.” J. Geodyn. 48 (3–5): 360–365. https://doi.org/10.1016/j.jog.2009.09.026.
Roshni, T., M. K. Jha, R. C. Deo, and A. Vandana. 2019. “Development and evaluation of hybrid artificial neural network architectures for modeling spatio-temporal groundwater fluctuations in a complex aquifer system.” Water Resour. Manage. 33 (9): 2381–2397. https://doi.org/10.1007/s11269-019-02253-4.
Sahoo, S., and M. K. Jha. 2013. “Groundwater-level prediction using multiple linear regression and artificial neural network techniques: A comparative assessment.” Hydrogeol. J. 21 (8): 1865–1887. https://doi.org/10.1007/s10040-013-1029-5.
Sahoo, S., T. A. Russo, J. Elliott, and I. Foster. 2017. “Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US.” Water Resour. Res. 53 (5): 3878–3895. https://doi.org/10.1002/2016WR019933.
Schoukens, K., R. Pintelon, E. Van Der Ouderaa, and J. Renneboog. 1988. “Survey of excitation signals for FFT based signal analyzers.” IEEE Trans. Instrum. Meas. 37 (3): 342–352. https://doi.org/10.1109/19.7453.
Sedghi, M. M., N. Samani, and B. Sleep. 2009. “Three-dimensional semi-analytical solution to groundwater flow in confined and unconfined wedge-shaped aquifers.” Adv. Water Resour. 32 (6): 925–935. https://doi.org/10.1016/j.advwatres.2009.03.004.
Seo, Y., S. Kim, O. Kisi, and V. P. Singh. 2015. “Daily water level forecasting using wavelet decomposition and artificial intelligence techniques.” J. Hydrol. 520 (Jan): 224–243. https://doi.org/10.1016/j.jhydrol.2014.11.050.
Serrano, S. E., and S. R. Workman. 1998. “Modeling transient stream/aquifer interaction with the non-linear Boussinesq equation and its analytical solution.” J. Hydrol. 206 (3–4): 145–255. https://doi.org/10.1016/S0022-1694(98)00111-5.
Shahriar, M. A., N. Sivakugan, B. M. Das, A. Urquhart, and M. Tapiolas. 2014. “Water table correction factors for settlements of shallow foundations in granular soils.” Int. J. Geomech. 15 (1): 06014015. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000391.
Shamseldin, A. Y. 1997. “Application of a neural network technique to rainfall-runoff modelling.” J. Hydrol. 199 (3–4): 272–294. https://doi.org/10.1016/S0022-1694(96)03330-6.
Tapoglou, E., G. P. Karatzas, I. C. Trichakis, and E. A. Varouchakis. 2014. “A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation.” J. Hydrol. 519 (Nov): 3193–3203. https://doi.org/10.1016/j.jhydrol.2014.10.040.
Taylor, C. J., and W. M. Alley. 2002. “Ground-water-level monitoring and the importance of long-term water-level data.” Accessed March 11, 2017. https://pubs.usgs.gov/circ/circ1217/pdf/pt1.pdf.
Uddameri, V. 2007. “Using statistical and artificial neural network models to forecast potentiometric levels at a deep well in South Texas.” Environ. Geol. 51 (6): 885–895. https://doi.org/10.1007/s00254-006-0452-5.
Vu, M. T., A. Jardani, N. Massei, and M. Fournier. 2021. “Reconstruction of missing groundwater level data by using long short-term memory (LSTM) deep neural network.” J. Hydrol. 597 (Jun): 125776. https://doi.org/10.1016/j.jhydrol.2020.125776.
WAMIS (Water Resources Management Information System). 2003. “Water resources management information system.” Accessed April 3, 2017. www.wamis.go.kr.
Wang, W., and S. Ding. 2003. “Wavelet network model and its application to the predication of hydrology.” Nat. Sci. 1 (1): 67–71.
Workman, S. R., S. E. Serrano, and K. Liberty. 1997. “Development and application of an analytical model of stream/aquifer interaction.” J. Hydrol. 200 (1–4): 149–163. https://doi.org/10.1016/S0022-1694(97)00014-0.
Xu, Y., C. F. Leung, J. Yu, and W. Chen. 2020. “Centrifuge model study on settlement of strip footing subject to rising water table in loess.” Can. Geotech. J. 57 (7): 992–1005. https://doi.org/10.1139/cgj-2018-0337.
Yasuhara, K., S. Murakami, N. Mimura, H. Komine, and J. Recio. 2007. “Influence of global warming on coastal infrastructural instability.” Sustainability Sci. 2 (1): 13–25. https://doi.org/10.1007/s11625-006-0015-4.
Zhan, G., and J. Cao. 2000. “Analytical and semi-analytical solutions of horizontal well capture times under no-flow and constant-head boundaries.” Adv. Water Resour. 23 (8): 835–848. https://doi.org/10.1016/S0309-1708(00)00014-2.
Zhang, J., Y. Zhu, X. Zhang, M. Ye, and J. Yang. 2018. “Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas.” J. Hydrol. 561 (Jun): 918–929. https://doi.org/10.1016/j.jhydrol.2018.04.065.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 27 • Issue 10 • October 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: May 25, 2021
Accepted: May 23, 2022
Published online: Aug 8, 2022
Published in print: Oct 1, 2022
Discussion open until: Jan 8, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.