참고문헌 (50)

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. 

2. 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. 

3. 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. 

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. 

   상세보기

5. 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. 

   상세보기

6. Brunner, P., and Simmons, C.T. (2012). "HydroGeoSphere: A fully integrated, physically based hydrological model." Groundwater, Vol. 50, No. 2, pp. 170-176. 

7. 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. 

8. 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. 

   상세보기

9. 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. 

   상세보기

10. 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. 

    상세보기

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. 

12. 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. 

13. 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. 

    상세보기

14. 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. 

    상세보기

15. Jeju Special Self-Governing Province (JSSGP) (2018). Comprehensive water resources management plan in Jeju Island. pp. 1-328. 

16. Jeong, J., and Park, E. (2019). "Comparative applications of datadriven models representing water table fluctuations." Journal of Hydrology, Vol. 572, pp. 261-273. 

17. 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. 

18. 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. 

19. 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. 

    상세보기

20. 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. 

    상세보기

21. 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. 

    상세보기

22. 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. 

    상세보기

23. 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. 

24. 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. 

    상세보기

25. 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. 

26. 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. 

27. 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. 

    상세보기

28. 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. 

    상세보기

29. 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. 

30. 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. 

    상세보기

31. 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. 

    상세보기

32. 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. 

    상세보기

33. Nourani, V., and Mousavi, S. (2016). "Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method." Journal of Hydrology, Vol. 536, pp. 10-25. 

34. 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. 

    상세보기

35. 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. 

36. 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. 

37. 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. 

    상세보기

38. 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. 

39. 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. 

40. 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. 

    상세보기

41. 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. 

42. Todd, D.K., and Larry, W.M. (2004). Groundwater hydrology, Third Edition. John Wiley & Sons Inc., Hoboken, NJ, USA, pp. 1-656. 

43. 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. 

    상세보기

44. 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. 

45. 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. 

46. 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. 

47. 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. 

    상세보기

48. 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. 

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. 

    상세보기

50. 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. 

저자의 다른 논문 :

강경구 (2)