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2021 Vol.54, Issue 12S Preview Page

Research Article

31 December 2021. pp. 1143-1154
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. 10.3390/w12010005
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. 10.1007/s11269-015-1163-z
Bengio, Y., Simard, P., and Frasconi, P. (1994). "Learning long-term dependencies with gradient descent is difficult." IEEE Transactions on Neural Networks, Vol. 5, No. 2, pp. 157-166. 10.1109/72.27918118267787
Chang, J., Wang, G., and Mao, T. (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. 10.1016/j.jhydrol.2015.09.038
Chollet, F. Allaire, J.J. (2018). Deep learning with R, Manning Publications, Shelter Island, NY, U.S., p. 360.
Clevert, D.A., Unterthiner, T., and Hochreiter, S. (2016). "Fast and accurate deep network learning by exponential linear units (ELUs)." arXiv preprint arXiv:1511.07289.
Coulibaly, P., Anctil, F., Aravena, R., and Bobée, B. (2001). "Artificial neural network modeling of water table depth fluctuations." Water Resources Research, Vol. 37, No. 4, pp. 885-896. 10.1029/2000WR900368
Emamgholizadeh, S., Moslemi, K., and Karami, G. (2014). "Prediction the groundwater level of bastam plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)." Water Resources Management, Vol. 28, No. 15, pp. 5433-5446. 10.1007/s11269-014-0810-0
Falbel, D., Allaire, J.J., Chollet, F., Tang, Y., Van Der Bijl, W., Studer, M., Keydana, S. (2019). R interface to 'Keras'. R package version, accessed on 5 April 2019, <>.
Hahnloser, R.H., Sarpeshkar, R., Mahowald, M.A., Douglas, R.J., and Seung, H.S. (2000). "Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit." Nature, Vol. 405, No. 6789, pp. 947-951. 10.1038/3501607210879535
Haykin, S. (2009). Neural networks and learning machines, Pearson Prentice Hall, Upper Saddle River, NJ, U.S.
Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp. 1735-1780. 10.1162/neco.1997.9.8.17359377276
Hosseini, Z., Gharechelou, S., Nakhaei, M., and Gharechelou, S. (2016). "Optimal design of BP algorithm by ACOR model for groundwater-level forecasting: A case study on Shabestar plain, Iran." Arabian Journal of Geosciences, Vol. 9, No. 6, p. 436. 10.1007/s12517-016-2454-2
Jeju Special Self-Governing Province (JSSGP) (2018). Comprehensive water resources management plan in Jeju Island. pp. 1-328.
Jeong, J., and Park, E. (2019). "Comparative applications of data-driven models representing water table fluctuations." Journal of Hydrology, Vol. 572, pp. 261-273. 10.1016/j.jhydrol.2019.02.051
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. 10.1002/hyp.10166
Kim, G.B., and Oh, D.H. (2018). "Determination of the groundwater yield of horizontal wells using an artificial neural network model incorporating riverside groundwater level data." The Journal of Engineering Geology, Vol. 28, No. 4, pp. 583-592.
Kim, J., Jun, S.M., Hwang, S., Kim, H.K., Heo, J., and Kang, M.S. (2021a). "Impact of activation functions on flood forecasting model based on artificial neural networks." Journal of The Korean Society of Agricultural Engineers, Vol. 63, No. 1, pp. 11-25.
Kim, M., Choi, J.Y., Bang, J., Yoon, P., and Kim, K. (2021b). "Comparison of artificial neural network model capability for runoff estimation about activation functions." Journal of The Korean Society of Agricultural Engineers, Vol. 63, No. 1, pp. 103-116.
Kim, T.W., and Valdés, J.B. (2003). "Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks." Journal of Hydrologic Engineering, Vol. 8, No. 6, pp. 319-328. 10.1061/(ASCE)1084-0699(2003)8:6(319)
Kingma, D.P., and Ba, J. (2014). "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980.
Klemeš, V. (1986). "Operational testing of hydrological simulation models." Hydrological Sciences Journal, Vol. 31, No. 1, pp. 13-24. 10.1080/02626668609491024
Krishna, B., Satyaji Rao, Y.R., and Vijaya, T. (2008). "Modelling groundwater levels in an urban coastal aquifer using artificial neural networks." Hydrological Processes, Vol. 22, No. 8, pp. 1180-1188. 10.1002/hyp.6686
Le, X.H., Ho, H.V., Lee, G., and Jung, S. (2019). "Application of long short-term memory (LSTM) neural network for flood forecasting." Water, Vol. 11, No. 7, p. 1387. 10.3390/w11071387
Lee, S., Lee, K.K., and Yoon, H. (2019). "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. 10.1007/s10040-018-1866-3
Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013). "Rectifier nonlinearities improve neural network acoustic models." Proceedings of the 30 th International Conference on Machine Learning, Atlanta, GA, U.S., Vol. 30, No. 1, p. 3.
Maier, H.R., and Dandy, G.C. (2000). "Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications." Environmental Modelling & Software, Vol. 15, No. 1, pp. 101-124. 10.1016/S1364-8152(99)00007-9
Maxwell, R.M., Condon, L.E., and Kollet, S.J. (2015). "A high-resolution 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. 10.5194/gmd-8-923-2015
McDonald, M.G., and Harbaugh, A.W. (1988). A modular three-dimensional finite-difference ground-water flow model. Vol. 6. US Geological Survey, Reston, VA, U.S.
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 Kathajodi - Surua Inter-basin of Odisha, India." Journal of Hydrology, Vol. 495, pp. 38-51. 10.1016/j.jhydrol.2013.04.041
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. 10.13031/2013.23153
Müller, J., Park, J., Sahu, R., Varadharajan, C., Arora, B., Faybishenko, B., and Agarwal, D. (2021). "Surrogate optimization of deep neural networks for groundwater predictions." Journal of Global Optimization, Vol. 81, No. 1, pp. 203-231. 10.1007/s10898-020-00912-0
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. 10.1016/0022-1694(70)90255-6
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. 10.1007/s11269-006-4007-z
Prechelt, L. (2012) "Early stopping - But when?." Neural networks: Tricks of the trade, Edited by Montavon G., Orr G.B., and Müller KR., Springer, Berlin Heidelberg, pp. 53-67. 10.1007/978-3-642-35289-8_5
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. 10.1016/j.jhydrol.2018.12.037
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. 10.1007/s13369-012-0291-5
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. 10.1007/s10040-013-1029-5
Sahoo, S., Russo, T.A., Elliott, J., and Foster, I. (2017). "Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US." Water Resources Research, Vol. 53, No. 5, pp. 3878-3895. 10.1002/2016WR019933
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. 10.3390/hydrology7030064
Sit, M., Demiray, B.Z., Xiang, Z., Ewing, G.J., Sermet, Y., and Demir, I. (2020). "A comprehensive review of deep learning applications in hydrology and water resources." Water Science and Technology, Vol. 82, No. 12, pp. 2635-2670. 10.2166/wst.2020.36933341760
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. 10.5194/hess-20-1405-2016
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. 10.1016/j.engappai.2012.02.009
Todd, D.K. and Larry, W.M. (2004). Groundwater hydrology, Third edition. John Wiley & Sons Inc., Hoboken, NJ, U.S., pp. 1-656.
Ukkonen, P., and Mäkelä, A. (2019). "Evaluation of machine learning classifiers for predicting deep convection." Journal of Advances in Modeling Earth Systems, Vol. 11, No. 6, pp. 1784-1802. 10.1029/2018MS001561
Vu, M.T., Jardani, A., Massei, N., and Fournier, M. (2021). "Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network." Journal of Hydrology, Vol. 597, p. 125776. 10.1016/j.jhydrol.2020.125776
Wen, X., Feng, Q., Deo, R.C., Wu, M., and Si, J. (2017). "Wavelet analysis - artificial neural network conjunction models for multi-scale monthly groundwater level predicting in an arid inland river basin, northwestern China." Hydrology Research, Vol. 48, No. 6, pp. 1710-1729. 10.2166/nh.2016.396
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. 10.1002/2013WR014767
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. 10.1111/gwat.1295731667821
Worland, S.C., Steinschneider, S., Asquith, W., Knight, R., and Wieczorek, M. (2019). "Prediction and inference of flow duration curves using multioutput neural networks." Water Resources Research, Vol. 55, No. 8, pp. 6850-6868. 10.1029/2018WR024463
Xu, B., Wang, N., Chen, T., and Li, M. (2015). "Empirical evaluation of rectified activations in convolutional network." arXiv preprint arXiv:1505.00853.
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 & Geosciences, Vol. 90, pp. 144-155. 10.1016/j.cageo.2016.03.002
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. 10.1016/j.jhydrol.2010.11.002
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. 10.1007/s11269-017-1811-6
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 54
  • No :12
  • Pages :1143-1154
  • Received Date :2021. 09. 27
  • Revised Date :2021. 11. 09
  • Accepted Date : 2021. 11. 18