Alley, W.M., Healy, R.W., LaBaugh, J.W., and Reilly, T.E. (2002). "Flow and storage in groundwater systems." Science, Vol. 296, No. 5575, pp. 1985-1990.10.1126/science.106712312065826
Behzad, M., Asghari, K., and Coppola, E.A. (2010). "Comparative study of SVMs and ANNs in aquifer water level prediction." Journal of Computing in Civil Engineering, Vol. 24, No. 5, pp. 408-413.10.1061/(ASCE)CP.1943-5487.0000043
Brownlee, J. (2018). Better Deep Learning: Train Faster, Reduce Overfitting, and Make better Predictions. Machine Learning Mastery, p. 540.
Chollet, F. (2017). Deep Learning with Python. Manning Publications, p. 384.
Chu, H.J., and Chang, L.C. (2009). "Application of optimal control and fuzzy theory for dynamic groundwater remediation design." Water Resources Management, Vol. 23, No. 4, pp. 647-660.10.1007/s11269-008-9293-1
Coppola, E., Szidarovszky, F., Poulton, M., and Charles, E. (2003). "Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions." Journal of Hydrologic Engineering, Vol. 8, No. 6, pp. 348-360.10.1061/(ASCE)1084-0699(2003)8:6(348)
Davis, J.C. (2002). Statistics and Data Analysis in Geology. 3rd editon, John Wiley & Sons, N.Y., U.S., p. 638.
Helmus, J. (2018). TensorFlow in Anaconda, accessed 11 November 2018, <https://www.anaconda.com/bolg/developer-blog/tensorflow- in-anaconda>.
Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp.1735-1780.10.1162/neco.19126.96.36.19959377276
Jung, S., Cho, H., Kim, J., and Lee, G. (2018). "Prediction of water level in a tidal river using a deep-learning based LSTM model." Journal of Korea Water Resources Association, Vol. 51, No. 12, pp. 1207-1216.
Kingma, D., and Ba, J. (2015). "Adam: A method for stochastic optimization", The 3rd international Conference for Learning Representations, San Diego, C.A., U.S.
Nikolos, I.K., Stergiadi, M., Papadopoulou, M.P., and Karatzas, G.P. (2008). "Artificial neural networks as an alternative approach to groundwater numerical modelling and environmental design." Hydrological Processes, Vol. 22, No. 17, pp. 3337-3348.10.1002/hyp.6916
NVIDIA (2016). NVIDIA Tesla P100 Whitepaper. NVIDIA, p. 45.
Olah, C. (2015). Understanding LSTM Networks, accessed 11 November 2018, <https://colah.github.io/posts/2015-08-Under standing-LSTMs>.
Parkin, G., Birkinshaw, S.J., Younger, P.L., Rao, Z., and Kirk, S. (2007). "A numerical modelling and neural network approach to estimate the impact of groundwater abstractions on river flows." Journal of Hydrology, Vol. 339, No. 1-2, pp. 15-28.10.1016/j.jhydrol.2007.01.041
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 U.S." Water Resources Research, Vol. 53, pp. 3878-3895.10.1002/2016WR019933
Song, S.-H., Choi, K.-J., and Kim, J.-S. (2013). "Evaluation of regional characteristics using time-series data of groundwater level in Jeju Island." Journal of the Environmental Sciences International, Vol. 22, No. 5, pp. 609-623.10.5322/JESI.2013.22.5.609
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2014). "Dropout: A simple way to prevent neural networks from overfitting." Journal of Machine Learning Research, Vol. 15, pp.1929-1958.
Sung, J., Lee, J., Chung, I.-M., and Heo, J.-H. (2017). "Hourly water level forecasting at tributary affected by main river condition." Water, Vol. 9, p. 644.10.3390/w9090644
Tran, Q., and Song, S. (2017). "Water level forecasting based on deep learning: A use case of trinity river-Texas-The United States." The Journal of Korean Institute of Information Scientists and Engineers, Vol. 44, No. 6, pp. 607-612.10.5626/JOK.2017.44.6.607
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
Yoon, P., Yoon, H., Kim, Y., and Kim, G. (2014). "A comparative study on forecasting groundwater level fluctuations of national groundwater monitoring networks using TFNM, ANN, and ANFIS." Journal of soil and groundwater environment, Vol. 19, No. 3, pp. 123-133.10.7857/JSGE.2014.19.3.123
Zhang, Z., 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 area." Journal of Hydrology, Vol. 561, pp. 918-929.10.1016/j.jhydrol.2018.04.065
- Publisher :KOREA WATER RESOURECES ASSOCIATION
- Publisher(Ko) :한국수자원학회
- Journal Title :Journal of Korea Water Resources Association
- Journal Title(Ko) :한국수자원학회 논문집
- Volume : 53
- No :4
- Pages :273-283
- Received Date :2020. 02. 06
- Revised Date :2020. 03. 18
- Accepted Date : 2020. 03. 18