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2025 Vol.58, Issue 10 Preview Page

Research Article

Impact of an ensemble of artificial intelligence models on improving groundwater level prediction in mid-mountainous region of Jeju Island

인공지능 모델의 앙상블이 제주도 중산간지역 지하수위 예측 향상에 미치는 영향

Mun-Ju Shina*
,
Jeong-Hun Kimb
,
Jeong Wook Kimc
,
Hyuk Joon Kohd
,
Soo-Hyoung Moone
신 문주a*
,
김 정훈b
,
김 정욱c
,
고 혁준d
,
문 수형e
aSenior Researcher, Drinking Water Research Laboratory, Jeju Special Self-Governing Province Development Corporation, Jeju, Korea
bResearcher, Drinking Water Research Laboratory, Jeju Special Self-Governing Province Development Corporation, Jeju, Korea
cResearcher, Drinking Water Research Laboratory, Jeju Special Self-Governing Province Development Corporation, Jeju, Korea
dChief Researcher, Drinking Water Research Laboratory, Jeju Special Self-Governing Province Development Corporation, Jeju, Korea
eDirector, R&D Innovation Division, Jeju Special Self-Governing Province Development Corporation, Jeju, Korea
a제주특별자치도개발공사 먹는물연구소 선임연구원
b제주특별자치도개발공사 먹는물연구소 연구원
c제주특별자치도개발공사 먹는물연구소 연구원
d제주특별자치도개발공사 먹는물연구소 책임연구원
e제주특별자치도개발공사 R&D혁신본부 본부장
*Corresponding Author
31 October 2025. pp. 911-926
PDF XML Citation
Abstract

Groundwater is a water resource that can be used for various purposes along with surface water. In the case of Jeju Island, groundwater is an essential water resource, so accurate groundwater level prediction and management for the distant future are necessary for sustainable groundwater use. In this study, groundwater levels were predicted using three artificial intelligence(LSTM, GRU, ANN) models for accurate long-term(1-3 months) future monthly groundwater level predictions for two groundwater level monitoring wells located in the mid-mountainous region of the Pyoseon watershed in Jeju Island. Afterwards, an ensemble model was used to analyze the improvement in groundwater level prediction for the entire data period and the low groundwater level period(November to May). As a result, the AI ​​models and the ensemble model appropriately predicted future groundwater levels(1 to 3 months) for the entire data period, and the ensemble model showed higher prediction performance than the individual AI models. The superiority of the groundwater level prediction performance of the three AI models varied by monitoring well and future prediction period, and a specific AI model did not always show the highest groundwater level prediction performance. Therefore, for more improved groundwater level prediction, an ensemble model that utilizes the results of different artificial intelligence models is needed. The groundwater level prediction performance for the low groundwater level period was higher than that for the entire data period. This means that the AI ​​models and the ensemble model are more suitable for groundwater level prediction during the low groundwater level period, which mostly corresponds to the groundwater level recession curve period. In particular, the ensemble model showed an appropriate NSE value of 0.7184 or higher for 3-month predictions, and this model produced prediction results with an NSE value that was improved by up to 0.1434 compared to individual AI models. This supports the importance and necessity of using ensemble models for accurate prediction of low groundwater levels in the distant future.

Keywords
Improvement of long-term groundwater level prediction
LSTM
GRU
ANN
Ensemble

지하수는 지표수와 함께 다양한 용수로 사용할 수 있는 수자원이며, 제주도의 경우 지하수는 필수불가결한 수자원이므로 지속가능한 지하수 이용을 위해 가능한 먼 미래의 정확한 지하수위 예측 및 관리가 필요하다. 본 연구에서는 제주도 내 표선유역 중산간지역에 위치한 2개 지하수위 관측정에 대해 정확한 장기간(1~3개월) 미래 월단위 지하수위 예측을 위해 3개 인공지능(LSTM, GRU, ANN) 모델을 사용하여 지하수위를 예측하고, 앙상블 모델을 사용하여 지하수위 예측 개선효과를 전체 데이터 기간과 낮은 지하수위 기간(11~5월)에 대해 분석하였다. 그 결과, 전체 데이터 기간에 대해 인공지능 모델과 앙상블 모델은 1~3개월 미래 지하수위를 적절히 예측하였으며, 앙상블 모델은 개별 인공지능 모델보다 높은 예측성능을 나타냈다. 3개 인공지능 모델의 지하수위 예측성능의 우열은 관측정별로 그리고 미래 예측기간별로 서로 달라 특정 인공지능 모델이 항상 가장 높은 지하수위 예측성능을 나타내지 않았으며, 따라서 더욱 개선된 지하수위 예측을 위해서는 서로 다른 인공지능 모델의 결과를 활용하는 앙상블 모델이 필요하다. 낮은 지하수위 기간에 대한 지하수위 예측성능은 전체 데이터 기간에 대한 결과보다 높았으며, 이것은 인공지능 모델과 앙상블 모델이 대부분 지하수위 감수곡선 기간에 해당하는 낮은 지하수위 기간의 지하수위 예측에 대해 더욱 적합하다는 것을 의미한다. 특히, 앙상블 모델은 3개월 예측에 대해 0.7184 이상의 적절한 NSE 값을 나타내었으며, 개별 인공지능 모델 대비 NSE 값이 최대 0.1434만큼 개선된 예측결과를 도출하였다. 이것은 먼 미래 낮은 지하수위의 보다 정확한 예측을 위한 앙상블 모델 사용의 중요성 및 필요성을 뒷받침한다.

키워드
장기 지하수위 예측 개선
LSTM
GRU
ANN
앙상블
References
1

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.

10.1016/j.jhydrol.2011.06.013
2

Afan, H.A., Ibrahem Ahmed Osman, A., Essam, Y., Ahmed, A.N., Huang, Y.F., Kisi, O., Sherif, M., Sefelnasr, A., Chau, K., and El-Shafie, A. (2021). “Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques.” Engineering Applications of Computational Fluid Mechanics, Vo. 15, No. 1, pp. 1420-1439.

10.1080/19942060.2021.1974093
3

Alizamir, M., Kisi, O., and Zounemat-Kermani, M. (2018). “Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data.” Hydrological Sciences Journal, Vol. 63, No. 1, pp. 63-73.

10.1080/02626667.2017.1410891
4

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
5

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.

10.1109/72.279181
6

Bizhanimanzar, M., Leconte, R., and Nuth, M. (2019). “Modelling of shallow water table dynamics using conceptual and physically based integrated surface-water - groundwater hydrologic models.” Hydrology and Earth System Sciences, Vol. 23, No. 5, pp. 2245-2260.

10.5194/hess-23-2245-2019
7

Chidepudi, S.K.R., Massei, N., Jardani, A., Dieppois, B., Henriot, A., and Fournier, M. (2025). “Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information?.” Hydrology and Earth System Sciences, Vol. 29, No. 4, pp. 841-861.

10.5194/hess-29-841-2025
8

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). “Learning phrase representations using RNN encoder-decoder for statistical machine translation.” arXiv preprint arXiv., 1406.1078.

10.3115/v1/D14-1179
9

Chollet, F., and Allaire, J.J. (2018). Deep learning with R; Manning Publications, Shelter Island, NY, U.S.

10

Davoudi Moghaddam, D., Rahmati, O., Haghizadeh, A., and Kalantari, Z. (2020). “A modeling comparison of groundwater potential mapping in a mountain bedrock aquifer: QUEST, GARP, and RF models.” Water, Vol. 12, No. 3, 679.

10.3390/w12030679
11

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 2.2.4.1, accessed 5 April 2019, <https://CRAN.R-project.org/package=keras>.

12

Fallah-Mehdipour, E., Haddad, O.B., and Mariño, M.A. (2013). “Prediction and simulation of monthly groundwater levels by genetic programming.” Journal of Hydro-Environment Research, Vol. 7, No. 4, pp. 253-260.

10.1016/j.jher.2013.03.005
13

Georgakakos, K.P., Seo, D.J., Gupta, H., Schaake, J., and Butts, M.B. (2004). “Towards the characterization of streamflow simulation uncertainty through multimodel ensembles.” Journal of Hydrology, Vol. 298, No. 1-4, pp. 222-241.

10.1016/j.jhydrol.2004.03.037
14

Gharehbaghi, A., Ghasemlounia, R., Ahmadi, F., and Albaji, M. (2022). “Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks.” Journal of Hydrology, Vol. 612, 128262.

10.1016/j.jhydrol.2022.128262
15

Gholizadeh, H., Zhang, Y., Frame, J., Gu, X., and Green, C.T. (2023). “Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama.” Science of the Total Environment, Vol. 901, 165884.

10.1016/j.scitotenv.2023.165884
16

Gong, Y., Zhang, Y., Lan, S., and Wang, H. (2016). “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, pp. 375-391.

10.1007/s11269-015-1167-8
17

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.

10.1038/35016072
18

Han, Z., Li, F., Zhao, Y., and Liu, C. (2025). “Investigation into groundwater level prediction within a deep learning framework: Incorporating the spatial dynamics of adjacent wells.” Journal of Hydrology, Vol. 657, 133097.

10.1016/j.jhydrol.2025.133097
19

Haykin, S. (2009). Neural networks and learning machines. Pearson Prentice Hall, Upper Saddle River, NJ, U.S.

20

Hochreiter, S., and Schmidhuber, J. (1997). “Long short-term memory.” Neural Computation, Vol. 9, No. 8, pp. 1735-1780.

10.1162/neco.1997.9.8.1735
21

Huan, S. (2024). “Geographic heterogeneity of activation functions in urban real-time flood forecasting: Based on seasonal trend decomposition using Loess-temporal convolutional network- gated recurrent unit model.” Journal of Hydrology, Vol. 636, 131279.

10.1016/j.jhydrol.2024.131279
22

Jeihouni, E., Mohammadi, M., Eslamian, S., and Zareian, M.J. (2019). “Potential impacts of climate change on groundwater level through hybrid soft-computing methods: A case study - Shabestar Plain, Iran.” Environmental Monitoring and Assessment, Vol. 191, No. 10, 620.

10.1007/s10661-019-7784-6
23

Jeju Special Self-Governing Province (JSSGP) (2022). Basic plan for integrated water management for Jeju special self- governing province.

24

Jeong, J., and Jeong, J. (2024). “Applying transfer learning to improve the performance of deep learning - based groundwater level prediction model with insufficient training data.” Economic and Environmental Geology, Vol. 57, No. 5, pp. 551-562.

10.9719/EEG.2024.57.5.551
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.

10.1002/hyp.10166
26

Kim, D., Jang, C., Choi, J., and Kwak, J. (2023). “A case study: Groundwater level forecasting of the gyorae area in actual practice on jeju island using deep-learning technique.” Water, Vol. 15, No. 5, 972.

10.3390/w15050972
27

Kim, I., and Lee, J. (2022). “Performance analysis of ANN prediction for groundwater level considering regional‐specific influence components.” Groundwater, Vol. 60, No. 3, pp. 344-361.

10.1111/gwat.13156
28

Kim, I., Lee, J., Kim, J., Lee, H., and Lee, J. (2021). “Analysis of groundwater level prediction performance with influencing factors by artificial neural network.” Journal of the Korean Geotechnical Society, Vol. 37, No. 5, pp. 19-31.

29

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)
30

Kim, Y.O., Jeong, D., and Ko, I.H. (2006). “Combining rainfall- runoff model outputs for improving ensemble streamflow prediction.” Journal of Hydrologic Engineering, Vol. 11, No. 6, pp. 578-588.

10.1061/(ASCE)1084-0699(2006)11:6(578)
31

Kingma, D.P., and Ba, J. (2014). “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980.

32

Kow, P.Y., Liou, J.Y., Sun, W., Chang, L.C., and Chang, F.J. (2024). “Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models.” Journal of Environmental Management, Vol. 351, 119789.

10.1016/j.jenvman.2023.119789
33

Lallahem, S., Mania, J., Hani, A., and Najjar, Y. (2005). “On the use of neural networks to evaluate groundwater levels in fractured media.” Journal of Hydrology, Vol. 307, No. 1-4, pp. 92-111.

10.1016/j.jhydrol.2004.10.005
34

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, 1387.

10.3390/w11071387
35

Lin, H., Gharehbaghi, A., Zhang, Q., Band, S.S., Pai, H. T., Chau, K.W., and Mosavi, A. (2022). “Time series-based groundwater level forecasting using gated recurrent unit deep neural networks.” Engineering Applications of Computational Fluid Mechanics, Vol. 16, No. 1, pp. 1655-1672.

10.1080/19942060.2022.2104928
36

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
37

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.

38

Mirzaei, M., and Shirmohammadi, A. (2024). “Utilizing data-driven approaches to forecast fluctuations in groundwater table.” Water, Vol. 16, No. 11, 1500.

10.3390/w16111500
39

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
40

Mohanty, S., Jha, M.K., Kumar, A., and Sudheer, K.P. (2010). “Artificial neural network modeling for groundwater level forecasting in a river island of eastern India.” Water Resources Management, Vol. 24, pp. 1845-1865.

10.1007/s11269-009-9527-x
41

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
42

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
43

Nan, T., Cao, W., Wang, Z., Gao, Y., Zhao, L., Sun, X., and Na, J. (2023). “Evaluation of shallow groundwater dynamics after water supplement in North China Plain based on attention- GRU model.” Journal of Hydrology, Vol. 625, 130085.

10.1016/j.jhydrol.2023.130085
44

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
45

Park, C., and Chung, I.M. (2020). “Evaluating the groundwater prediction using LSTM model.” Journal of Korea Water Resources Association, Vol. 53, No. 4, pp. 273-283.

10.3741/JKWRA.2020.53.4.273
46

Payne, K., Chami, P., Odle, I., Yawson, D.O., Paul, J., Maharaj- Jagdip, A., and Cashman, A. (2022). “Machine learning for surrogate groundwater modelling of a small carbonate island.” Hydrology, Vol. 10, No. 1, 2.

10.3390/hydrology10010002
47

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, pp. 53-67.

10.1007/978-3-642-35289-8_5
48

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
49

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
50

Roy, D.K., Biswas, S.K., Mattar, M.A., El-Shafei, A.A., Murad, K.F.I., Saha, K.K., Datta, B., and Dewidar, A.Z. (2021). “Groundwater level prediction using a multiple objective genetic algorithm-grey relational analysis based weighted ensemble of ANFIS models.” Water, Vol. 13, No. 21, 3130.

10.3390/w13213130
51

Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986). “Learning representations by back-propagating errors.” Nature, Vol. 323, No. 6088, pp. 533-536.

10.1038/323533a0
52

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
53

Seidu, J., Ewusi, A., Kuma, J.S.Y., Ziggah, Y.Y., and Voigt, H.J. (2023). “Impact of data partitioning in groundwater level prediction using artificial neural network for multiple wells.” International Journal of River Basin Management, Vol. 21, No. 4, pp. 639-650.

10.1080/15715124.2022.2079653
54

Shin, M.J., Kim, J.H., Kang, S.Y., Moon, S.H., and Hyun, E.H. (2024). “Impact of Baekrokdam precipitation observation data on improving groundwater level prediction in mid-mountainous region of Jeju Island.” Journal of Korea Water Resources Association, Vol. 57, No. 10, pp. 673-686.

10.3741/JKWRA.2024.57.10.673
55

Shin, M.J., Kim, J.W., Moon, D.C., Lee, J.H., and Kang, K.G. (2021). “Comparative analysis of activation functions of artificial neural network for prediction of optimal groundwater level in the middle mountainous area of Pyoseon watershed in Jeju Island.” Journal of Korea Water Resources Association, Vol. 54, No. 12, pp. 1143-1154.

10.3741/JKWRA.2021.54.S-1.1143
56

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, 64.

10.3390/hydrology7030064
57

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.2020.369
58

Sun, J., Hu, L., Li, D., Sun, K., and Yang, Z. (2022). “Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management.” Journal of Hydrology, Vol. 608, 127630.

10.1016/j.jhydrol.2022.127630
59

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
60

Sušanj Čule, I., Ožanić, N., Volf, G., and Karleuša, B. (2025). “Artificial Neural Network (ANN) water-level prediction model as a tool for the sustainable management of the Vrana Lake (Croatia) water supply system.” Sustainability, Vol. 17, 722.

10.3390/su17020722
61

Tao, H., Hameed, M.M., Marhoon, H.A., Zounemat-Kermani, M., Heddam, S., Kim, S., Sulaiman, S.O., Tan, M.L., Sa’adi, Z. Mehr, A.D., et al. (2022). “Groundwater level prediction using machine learning models: A comprehensive review.” Neurocomputing, Vol. 489, pp. 271-308.

10.1016/j.neucom.2022.03.014
62

Yariyan, P., Janizadeh, S., Van Phong, T., Nguyen, H.D., Costache, R., Van Le, H., and Tiefenbacher, J.P. (2020). “Improvement of best first decision trees using Bagging and dagging ensembles for flood probability mapping.” Water Resources Management, Vol. 34, No. 9, pp. 3037-3053.

10.1007/s11269-020-02603-7
63

Yin, J., and Tsai, F.T.C. (2018). “Saltwater scavenging optimization under surrogate uncertainty for a multi-aquifer system.” Journal of Hydrology, Vol. 565, pp. 698-710.

10.1016/j.jhydrol.2018.08.021
64

Yin, J., Medellín-Azuara, J., Escriva-Bou, A., and Liu, Z. (2021). “Bayesian machine learning ensemble approach to quantify model uncertainty in predicting groundwater storage change.” Science of The Total Environment, Vol. 769, 144715.

10.1016/j.scitotenv.2020.144715
65

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
66

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
67

Yoon, H., Kim, Y., Ha, K., and Kim, G.B. (2013). “Application of groundwater-level prediction models using data-based learning algorithms to national groundwater monitoring network data.” The Journal of Engineering Geology, Vol. 23, No. 2, pp. 137-147.

10.9720/kseg.2013.2.137
68

Yoon, P., Yoon, H., Kim, Y., and Kim, G.B. (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
69

Zhang, J., Chen, X., Khan, A., Zhang, Y. K., Kuang, X., Liang, X., Taccari, M., and Nuttall, J. (2021). “Daily runoff forecasting by deep recursive neural network.” Journal of Hydrology, Vol. 596, 126067.

10.1016/j.jhydrol.2021.126067
70

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.

10.1016/j.jhydrol.2018.04.065
71

Zhou, R., and Zhang, Y. (2023). “Linear and nonlinear ensemble deep learning models for karst spring discharge forecasting.” Journal of Hydrology, Vol. 627, 130394.

10.1016/j.jhydrol.2023.130394
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 58
  • No :10
  • Pages :911-926
  • Received Date : 2025-07-30
  • Revised Date : 2025-09-18
  • Accepted Date : 2025-09-29
  • DOI :https://doi.org/10.3741/JKWRA.2025.58.10.911
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