Browse > Article
http://dx.doi.org/10.3741/JKWRA.2021.54.7.485

Estimation of the allowable range of prediction errors to determine the adequacy of groundwater level simulation results by an artificial intelligence model  

Shin, Mun-Ju (Water Resources Research Team, Jeju Province Development Corporation)
Moon, Soo-Hyoung (Water Resources Research Team, Jeju Province Development Corporation)
Moon, Duk-Chul (Water Resources Research Team, Jeju Province Development Corporation)
Ryu, Ho-Yoon (Water Resources Research Team, Jeju Province Development Corporation)
Kang, Kyung Goo (Research and Development Center, Jeju Province Development Corporation)
Publication Information
Journal of Korea Water Resources Association / v.54, no.7, 2021 , pp. 485-493 More about this Journal
Abstract
Groundwater is an important water resource that can be used along with surface water. In particular, in the case of island regions, research on groundwater level variability is essential for stable groundwater use because the ratio of groundwater use is relatively high. Researches using artificial intelligence models (AIs) for the prediction and analysis of groundwater level variability are continuously increasing. However, there are insufficient studies presenting evaluation criteria to judge the appropriateness of groundwater level prediction. This study comprehensively analyzed the research results that predicted the groundwater level using AIs for various regions around the world over the past 20 years to present the range of allowable groundwater level prediction errors. As a result, the groundwater level prediction error increased as the observed groundwater level variability increased. Therefore, the criteria for evaluating the adequacy of the groundwater level prediction by an AI is presented as follows: less than or equal to the root mean square error or maximum error calculated using the linear regression equations presented in this study, or NSE ≥ 0.849 or R2 ≥ 0.880. This allowable prediction error range can be used as a reference for determining the appropriateness of the groundwater level prediction using an AI.
Keywords
Artificial intelligence model; Groundwater level prediction; Allowable prediction error; Maximum fluctuation width of groundwater level; Linear regression analysis;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Mohanty, S., Jha, M.K., Kumar, A., and Panda, D.K. (2013). "Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in KathajodiSurua Inter-basin of Odisha, India." Journal of Hydrology, Vol. 495, pp. 38-51.   DOI
2 Nash, J.E., and Sutcliffe, J.V. (1970). "River flow forecasting through conceptual models part I-A discussion of principles." Journal of Hydrology, Vol. 10, No. 3, pp. 282-290.   DOI
3 Nourani, V., and Mousavi, S. (2016). "Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method." Journal of Hydrology, Vol. 536, pp. 10-25.   DOI
4 Kumar, D., Roshni, T., Singh, A., Jha, M.K., and Samui, P. (2020). "Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: A comparative study." Earth Science Informatics, Vol. 13, No. 4, pp. 1237-1250.   DOI
5 Mukherjee, A., and Ramachandran, P. (2018). "Prediction of GWL with the help of GRACE TWS for unevenly spaced time series data in India: Analysis of comparative performances of SVR, ANN and LRM." Journal of Hydrology, Vol. 558, pp. 647-658.   DOI
6 Rakhshandehroo, G.R., Vaghefi, M., and Aghbolaghi, M.A. (2012). "Forecasting groundwater level in Shiraz plain using artificial neural networks." Arabian Journal for Science and Engineering, Vol. 37, No. 7, pp. 1871-1883.   DOI
7 White, J.T., Knowling, M.J., and Moore, C.R. (2020). "Consequences of groundwater-model vertical discretization in risk-based decision-making." Groundwater, Vol. 58, No. 5, pp. 695-709.
8 Yoon, H., Hyun, Y., Ha, K., Lee, K.K., and Kim, G.B. (2016). "A method to improve the stability and accuracy of ANN-and SVM-based time series models for long-term groundwater level predictions." Computers and Geosciences, Vol. 90, pp. 144-155.
9 Yoon, H., Jun, S.C., Hyun, Y., Bae, G.O., and Lee, K.K. (2011). "A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer." Journal of Hydrology, Vol. 396, No. 1-2, pp. 128-138.   DOI
10 Sun, Y., Wendi, D., Kim, D.E., and Liong, S.Y. (2016). "Application of artificial neural networks in groundwater table forecasting-a case study in a Singapore swamp forest." Hydrology and Earth System Sciences, Vol. 20, No. 4. pp. 1405-1412.   DOI
11 Daliakopoulos, I.N., Coulibaly, P., and Tsanis, I.K. (2005). "Groundwater level forecasting using artificial neural networks." Journal of Hydrology, Vol. 309, No. 1-4, pp. 229-240.   DOI
12 Todd, D.K., and Larry, W.M. (2004). Groundwater hydrology, Third Edition. John Wiley & Sons Inc., Hoboken, NJ, USA, pp. 1-656.
13 White, J.T., Doherty, J.E., and Hughes, J.D. (2014). "Quantifying the predictive consequences of model error with linear subspace analysis." Water Resources Research, Vol. 50, No. 2, pp. 1152-1173.   DOI
14 Zhang, J., Zhu, Y., Zhang, X., Ye, M., and Yang, J. (2018). "Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas." Journal of Hydrology, Vol. 561, pp. 918-929.   DOI
15 Ala-Aho, P., Rossi, P.M., Isokangas, E., and Klove, B. (2015). "Fully integrated surface-subsurface flow modelling of groundwaterlake interaction in an esker aquifer: Model verification with stable isotopes and airborne thermal imaging." Journal of Hydrology, Vol. 522, pp. 391-406.   DOI
16 Brunner, P., and Simmons, C.T. (2012). "HydroGeoSphere: A fully integrated, physically based hydrological model." Groundwater, Vol. 50, No. 2, pp. 170-176.   DOI
17 Huang, F., Huang, J., Jiang, S.H., and Zhou, C. (2017). "Prediction of groundwater levels using evidence of chaos and support vector machine." Journal of Hydroinformatics, Vol. 19, No. 4, pp. 586-606.   DOI
18 Lee, S., Lee, K.K., and Yoon, H. (2018). "Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors." Hydrogeology Journal, Vol. 27, No. 2, pp. 567-579.   DOI
19 Kisi, O., and Shiri, J. (2012). "Wavelet and neuro-fuzzy conjunction model for predicting water table depth fluctuations." Hydrology Research, Vol. 43, No. 3, pp. 286-300.   DOI
20 Krishna, B., Satyaji Rao, Y.R., and Vijaya, T. (2008). "Modelling groundwater levels in an urban coastal aquifer using artificial neural networks." Hydrological Processes: An International Journal, Vol. 22, No. 8, pp. 1180-1188.   DOI
21 Sahoo, S., and Jha, M.K. (2013). "Groundwater-level prediction using multiple linear regression and artificial neural network techniques: A comparative assessment." Hydrogeology Journal, Vol. 21, No. 8, pp. 1865-1887.   DOI
22 Barzegar, R., Fijani, E., Moghaddam, A.A., and Tziritis, E. (2017). "Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models." Science of the Total Environment, Vol. 599, pp. 20-31.   DOI
23 Nie, S., Bian, J., Wan, H., Sun, X., and Zhang, B. (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, Vol. 66, No. 1, pp. 15-24.   DOI
24 Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., and Veith, T.L. (2007). "Model evaluation guidelines for systematic quantification of accuracy in watershed simulations." Transactions of the ASABE, Vol. 50, No. 3, pp. 885-900.   DOI
25 Jha, M.K., and Sahoo, S. (2014). "Efficacy of neural network and genetic algorithm techniques in simulating spatio-temporal fluctuations of groundwater." Hydrological Processes, Vol. 29, No. 5, pp. 671-691.   DOI
26 Adamowski, J., and Chan, H.F. (2011). "A wavelet neural network conjunction model for groundwater level forecasting." Journal of Hydrology, Vol. 407, No. 1-4, pp. 28-40.   DOI
27 Barthel, R., and Banzhaf, S. (2016). "Groundwater and surface water interaction at the regional-scale-a review with focus on regional integrated models." Water Resources Management, Vol. 30, No. 1, pp. 1-32.   DOI
28 Chang, F.J., Chang, L.C., Huang, C.W., and Kao, I.F. (2016). "Prediction of monthly regional groundwater levels through hybrid soft-computing techniques." Journal of Hydrology, Vol. 541, pp. 965-976.   DOI
29 Coulibaly, P., Anctil, F., Aravena, R., and Bobee, B. (2001). "Artificial neural network modeling of water table depth fluctuations." Water Resources Research, Vol. 37, No. 4, pp. 885-896.   DOI
30 Rahman, A.S., Hosono, T., Quilty, J.M., Das, J., and Basak, A. (2020). "Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms." Advances in Water Resources, Vol. 141, p. 103595.   DOI
31 Rajaee, T., Ebrahimi, H., and Nourani, V. (2019). "A review of the artificial intelligence methods in groundwater level modeling." Journal of Hydrology, Vol. 572, pp. 336-351.   DOI
32 Shin, M.J., Moon, S.H., Kang, K.G., Moon, D.C., and Koh, H.J. (2020). "Analysis of groundwater level variations caused by the changes in groundwater withdrawals using long short-term memory network." Hydrology, Vol. 7, No. 3, p. 64.   DOI
33 Nayak, P.C., Rao, Y.S., and Sudheer, K.P. (2006). "Groundwater level forecasting in a shallow aquifer using artificial neural network approach." Water Resources Management, Vol. 20, No. 1, pp. 77-90.   DOI
34 Taormina, R., Chau, K.W., and Sethi, R. (2012). "Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon." Engineering Applications of Artificial Intelligence, Vol. 25, No. 8, pp. 1670-1676.   DOI
35 Therrien, R. (1992). Three-dimensional analysis of variably saturated flow and solute transport in discretely-fractured porous media. Ph.D. thesis, University of Waterloo, Waterloo, Canada.
36 Wen, X., Feng, Q., Deo, R.C., Wu, M., and Si, J. (2017). "Wavelet analysis-artificial neural network conjunction models for multiscale monthly groundwater level predicting in an arid inland river basin, northwestern China." Hydrology Research, Vol. 48, No. 6, pp. 1710-1729.   DOI
37 He, Z., Zhang, Y., Guo, Q., and Zhao, X. (2014). "Comparative study of artificial neural networks and wavelet artificial neural networks for groundwater depth data forecasting with various curve fractal dimensions." Water Resources Management, Vol. 28, No. 15, pp. 5297-5317.   DOI
38 Chen, L.H., Chen, C.T., and Pan, Y.G. (2010). "Groundwater level prediction using SOM-RBFN multisite model." Journal of Hydrologic Engineering, Vol. 15, No. 8, pp. 624-631.   DOI
39 Mirzavand, M., Khoshnevisan, B., Shamshirband, S., Kisi, O., Ahmad, R., and Akib, S. (2015). "Evaluating groundwater level fluctuation by support vector regression and neuro-fuzzy methods: A comparative study." Natural Hazards, Vol. 1, No. 1, pp. 1-15.   DOI
40 Chen, L.H., Chen, C.T., and Lin, D.W. (2011). "Application of integrated back-propagation network and self-organizing map for groundwater level forecasting." Journal of Water Resources Planning and Management, Vol. 137, No. 4, pp. 352-365.   DOI
41 Jeong, J., and Park, E. (2019). "Comparative applications of datadriven models representing water table fluctuations." Journal of Hydrology, Vol. 572, pp. 261-273.   DOI
42 Gong, Y., Zhang, Y., Lan, S., and Wang, H. (2015). "A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee, Florida." Water Resources Management, Vol. 30, No. 1, pp. 375-391.   DOI
43 Jeju Special Self-Governing Province (JSSGP) (2018). Comprehensive water resources management plan in Jeju Island. pp. 1-328.
44 Afzaal, H., Farooque, A.A., Abbas, F., Acharya, B., and Esau, T. (2020). "Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning." Water, Vol. 12, No. 1, p. 5.   DOI
45 Juan, C., Genxu, W., and Tianxu, M. (2015). "Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model." Journal of Hydrology, Vol. 529, pp. 1211-1220.   DOI
46 Khalil, B., Broda, S., Adamowski, J., Ozga-Zielinski, B., and Donohoe, A. (2015). "Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models." Hydrogeology Journal, Vol. 23, No. 1, pp. 121-141.   DOI
47 Maxwell, R.M., Condon, L.E., and Kollet, S.J. (2015). "A highresolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3." Geoscientific Model Development, Vol. 8, No. 3, pp. 923-937.   DOI
48 McDonald, M.G., and Harbaugh, A.W. (1988). A modular threedimensional finite-difference ground-water flow model. Vol. 6. US Geological Survey, VA, U.S.
49 Yu, H., Wen, X., Feng, Q., Deo, R.C., Si, J., and Wu, M. (2018). "Comparative study of hybrid-wavelet artificial intelligence models for monthly groundwater depth forecasting in extreme arid regions, Northwest China." Water Resources Management, Vol. 32, No. 1, pp. 301-323.   DOI
50 Yang, Z.P., Lu, W.X., Long, Y.Q., and Li, P. (2009). "Application and comparison of two prediction models for groundwater levels: A case study in Western Jilin Province, China." Journal of Arid Environments, Vol. 73, No. 4-5, pp. 487-492.   DOI