All Issue

2021 Vol.54, Issue 10 Preview Page

Research Article

October 2021. pp. 795-805
Abstract
References
1
Ahmad, W., and Kim, D. (2019). "Estimation of flow in various sizes of streams using the Sentinel-1 Synthetic Aperture Radar (SAR) data in Han River Basin, Korea." International Journal of Applied Earth Observation and Geoinformation, Vol. 83, 101930. 10.1016/j.jag.2019.101930
2
Bai, Y., Bezak, N., Zeng, B., Li, C., Sapač, K., and Zhang, J. (2021). "Daily runoff forecasting using a cascade long short-term memory model that considers different variables." Water Resources Management, Vol. 35, No. 4, pp. 1167-1181. 10.1007/s11269-020-02759-2
3
Boulmaiz, T., Guermoui, M., and Boutaghane, H. (2020). "Impact of training data size on the LSTM performances for rainfall-runoff modeling." Modeling Earth Systems and Environment, Vol. 6, pp. 2153-2164. 10.1007/s40808-020-00830-w
4
Bowes, B.D., Sadler, J.M., Morsy, M.M., Behl, M., and Goodall, J.L. (2019). "Forecasting groundwater table in a flood prone coastal city with long short-term memory and recurrent neural networks." Water, Vol. 11, No. 5, 1098. 10.3390/w11051098
5
Chen, Z., Zhu, Z., Jiang, H., and Sun, S. (2020). "Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods." Journal of Hydrology, Vol. 591, 125286. 10.1016/j.jhydrol.2020.125286
6
Fang, K., and Shen, C. (2020). "Near-real-time forecast of satellite-based soil moisture using long short-term memory with an adaptive data integration kernel." Journal of Hydrometeorology, Vol. 21, No. 3, pp. 399-413. 10.1175/JHM-D-19-0169.1
7
Fang, K., Kifer, D., Lawson, K., and Shen, C. (2020). "Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions." Water Resources Research, Vol. 56, No. 12, e2020WR028095. 10.1029/2020WR028095
8
Han, J., Olivera, F., and Kim, D. (2021a). "An algorithm of spatial composition of hourly rainfall fields for improved high rainfall value estimation." KSCE Journal of Civil Engineering, Vol. 25, No. 1, pp. 356-368. 10.1007/s12205-020-0526-z
9
Han, H., Choi, C., Jung, J., and Kim, H.S. (2021b). "Application of sequence to sequence learning based LSTM model (LSTM-s2s) for forecasting dam inflow." Journal of Korea Water Resources Association, Vol. 54, No. 3, pp. 157-166.
10
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
11
Hou, A.Y., Kakar, R.K., Neeck, S., Azarbarzin, A.A., Kummerow, C.D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T. (2014). "The global precipitation measurement mission." Bulletin of the American Meteorological Society, Vol. 95, No. 5, pp. 701-722. 10.1175/BAMS-D-13-00164.1
12
Hu, C., Wu, Q., Li, H., Jian, S., Li, N., and Lou, Z. (2018). "Deep learning with a long short-term memory networks approach for rainfall-runoff simulation." Water, Vol. 10, No. 11, 1543. 10.3390/w10111543
13
Idrees, M., Jehanzaib, M., Kim, D., Kim, T. (2021a). "Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir." Stochastic Environmental Research and Risk Assessment, Vol. 35, pp. 1805-1823. doi: 10.1007/s00477-021-01982-6. 10.1007/s00477-021-01982-6
14
Idrees, B.M., Lee, J.-Y., Kim, D., Kim., T.-W. (2021b) "Complementary modeling approach for estimating sedimentation and hydraulic flushing parameters using artificial neural networks and RESCON2 Model." KSCE Journal of Civil Engineering, Vol. 25, pp. 3766-3778. doi: 10.1007/s12205-021-1877-9 10.1007/s12205-021-1877-9
15
Jehanzaib, M., Idrees, M.B., Kim, D., and Kim, T. (2021). "Comprehensive evaluation of machine learning techniques for hydrological drought forecasting." Journal of Irrigation and Drainage Engineering, Vol. 147, No. 7. doi: 10.1061/(ASCE) IR.1943-4774.0001575 10.1061/(ASCE)IR.1943-4774.0001575
16
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.
17
Jung, Y., Kim, D., Kim, D., Kim, M., and Lee, S.O. (2014). "Simplified flood inundation mapping based on flood elevation-discharge rating curves using satellite images in gauged watersheds." Water, Vol. 6, No. 5, pp. 1280-1299. 10.3390/w6051280
18
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M. (2018). "Rainfall-unoff modelling using long short-term memory (LSTM) networks." Hydrology and Earth System Sciences, Vol. 22, No. 11, pp. 6005-6022. 10.5194/hess-22-6005-2018
19
Lee, G., Kim, D., Kwon, H., and Choi, E. (2019). "Estimation of maximum daily fresh snow accumulation using an artificial neural network model." Advances in Meteorology, Vol. 2019, 2709351. doi: 10.1155/2019/2709351 10.1155/2019/2709351
20
Lee, Y.O., Jo, J., and Hwang, J. (2017). "Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection." 2017 IEEE International Conference on big data (big data), IEEE, Boston, MA, U.S., pp. 3248-3253. doi: 10.1109/BigData.2017.8258307 10.1109/BigData.2017.8258307
21
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.
22
Seo, M., Kim, D., Ahmad, W., and Cha, J.H. (2018). "Estimation of stream flow discharge using the satellite synthetic aperture radar images at the mid to small size streams." Journal of Korea Water Resources Association, Vol. 51, No. 12, pp. 1181-1194.
23
Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015). "Convolutional LSTM network: A machine learning approach for precipitation nowcasting." Advances in Neural Information Processing Systems, pp. 802-810.
24
Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2017). "Deep learning for precipitation nowcasting: A benchmark and a new model." arXiv Preprint, arXiv:1706.3458.
25
Silva, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T. and Hassabis, D. (2017). "Mastering the game of go without human knowledge." Nature, Vol. 550, No. 7676, pp. 354-359. 10.1038/nature2427029052630
26
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
27
Tomlinson, C.J., Chapman, L., Thornes, J.E., and Baker, C. (2011). "Remote sensing land surface temperature for meteorology and climatology: A review." Meteorological Applications, Vol. 18, No. 3, pp. 296-306. 10.1002/met.287
28
Yin, J., Deng, Z., Ines, A.V., Wu, J., and Rasu, E. (2020). "Forecast of short-erm daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)." Agricultural Water Management, Vol. 242, 106386. 10.1016/j.agwat.2020.106386
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 54
  • No :10
  • Pages :795-805
  • Received Date :2021. 07. 12
  • Revised Date :2021. 08. 05
  • Accepted Date : 2021. 08. 05