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2023 Vol.56, Issue 11 Preview Page

Research Article

30 November 2023. pp. 801-813
Ahn, S.J., Yeon, I.S., Han, Y.S., and Lee, J.K. (2001). "Water quality forecasting at Gongju station in Geum River using neural network model." Journal of Korea Water Resources Association, Vol. 34, No. 6, pp. 701-711.
Ahn, S.R., Ha, R., Yoon, S.W., and Kim, S.J. (2014). "Evaluation of future turbidity water and eutrophication in Chungju Lake by climate change using CE-QUAL-W2." Journal of Korea Water Resources Association, Vol. 47, No. 2, pp. 145-159. 10.3741/JKWRA.2014.47.2.145
Arras, L., Montavon, G., Müller, K.R., and Samek, W. (2017). "Explaining recurrent neural network predictions in sentiment analysis." arXiv, arXiv:1706.07206. 10.48550/arXiv.1706.07206
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., and Samek, W. (2015). "On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation." PloS One, Vol. 10, No. 7, e0130140. 10.1371/journal.pone.013014026161953PMC4498753
Carbone, G., Bortolussi, L., and Sanguinetti, G. (2022). "Resilience of bayesian layer-wise explanations under adversarial attacks." Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, pp. 1-8. 10.1109/IJCNN55064.2022.9892788
Chun, Y.E., Kim, S.B., Lee, J.Y., and Woo, J.H. (2021). "Study on credit rating model using explainable AI." The Korean Data & Information Science Society, Vol. 32, No. 2, pp. 283-295. 10.7465/jkdi.2021.32.2.283
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv, arXiv:1412.3555. 10.48550/arXiv.1412.3555
Dogan, E., Ates, A., Yilmaz, E.C., and Eren, B. (2008). "Application of artificial neural networks to estimate wastewater treatment plant inlet biochemical oxygen demand." Environmental Progress, Vol. 27, No. 4, pp. 439-446. 10.1002/ep.10295
Drolc, A., and Končan, J.Z. (1999). "Calibration of QUAL2E model for the Sava River (Slovenia)." Water Science and Technology, Vol. 40, No. 10, pp. 111-118. 10.2166/wst.1999.0509
Haghiabi, A.H., Nasrolahi, A.H., and Parsaie, A. (2018). "Water quality prediction using machine learning methods." Water Quality Research Journal, Vol. 53, No. 1, pp. 3-13. 10.2166/wqrj.2018.025
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
Kim, S.H., Park, J.H., and Kim, B. (2021). "Prediction of cyanobacteria harmful algal blooms in reservoir using machine learning and deep learning." Journal of Korea Water Resources Association, Vol. 54, No. 12, pp. 1167-1181. 10.3741/JKWRA.2022.55.S-1.1167
Lee, J., and Han, J. (2021). "Layer-wise Relevance Propagation (LRP) based technical and macroeconomic indicator impact analysis for an explainable deep learning model to predict an increase and decrease in KOSPI." Journal of Korean Institute of Information Scientists and Engineers, Vol. 48, No. 12, pp. 1289-1297. 10.5626/JOK.2021.48.12.1289
Lee, J.H., Kim, J.S., Jang, H.W., and Lee, J.C. (2013). "Drought forecasting using the multi layer perceptron (MLP) artificial neural network model." Journal of Korea Water Resources Association, Vol. 46, No. 12, pp. 1249-1263. 10.3741/JKWRA.2013.46.12.1249
Lee, S., and Lee, D. (2018). "Improved prediction of harmful algal blooms in four Major South Korea's Rivers using deep learning models." International Journal of Environmental Research and Public Health, Vol. 15, No. 7, 1322. 10.3390/ijerph1507132229937531PMC6069434
Lee, W.J., and Lee, E.H. (2023). "Improvement of multi layer perceptron performance using combination of adaptive moments and improved harmony search for prediction of Daecheong Dam inflow." Journal of Korea Water Resources Association, Vol. 56, No. 1, pp. 63-74. 10.3741/JKWRA.2023.56.1.63
Lim, H., An, H., Choi, E., and Kim, Y. (2020). "Prediction of the DO concentration using the machine learning algorithm: case study in Oncheoncheon, Republic of Korea." Korean Journal of Agricultural Science, Vol. 47, No. 4, pp. 1029-1037.
Lu, H., and Ma, X. (2020). "Hybrid decision tree-based machine learning models for short-term water quality prediction." Chemosphere, Vol. 249, 126169. 10.1016/j.chemosphere.2020.12616932078849
Luis, M.B., Sidinei, M.T., and Priscilla, C. (2010). "Limnological effects of Egeria najas Planchon (Hydrocharita-ceae) in the arms of Itaipu Reservoir (Brazil, Paraguay)." Limnology, Vol. 11, No. 1, pp. 39-47. 10.1007/s10201-009-0286-4
Lundberg, S.M., and Lee, S.I. (2017). "A unified approach to interpreting model predictions." Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, U.S., Vol. 30. pp. 4768-4777.
Luo, D.L. (2002). "Study on the distribution of dissolved oxygen in Shenhu Bay and its relationship with phytoplankton and suspended matter." Marine Science Bulletin, Vol. 21, No. 1, pp. 31-36.
Mahsa, M., and Lee, T. (2018). "Comparison of optimization algorithms in deep learning-based neural networks for hydrological forecasting: Case study of Nam River daily runoff." Journal of the Korean Society of Hazard Mitigation, Vol. 18, No. 6, pp. 377-384. 10.9798/KOSHAM.2018.18.6.377
McCulloch, W.S., and Pitts, W. (1943). "A logical calculus of the ideas immanent in nervous activity." The Bulletin of Mathematical Biophysics, Vol. 5, pp. 115-133. 10.1007/BF02478259
Mok, J.Y., Choi, J.H., and Moon, Y.I. (2020). "Prediction of multipurpose dam inflow using deep learning." Journal of Korea Water Resources Association, Vol. 53, No. 2, pp. 97-105. 10.3741/JKWRA.2020.53.2.97
Nahm, E-S. (2022). "Neural network modeling based XAI of activated sludge process in wastewater treatment system for dissolved oxygen control." The Transactions of the Korean Institute of Electrical Engineers, Vol. 71, No. 8, pp. 1176-1181. 10.5370/KIEE.2022.71.8.1176
Nawi, N.M., Atomi, W.H., and Rehman, M.Z. (2013). "The effect of data pre-processing on optimized training of artificial neural networks." Procedia Technology, Vol. 11, pp. 32-39. 10.1016/j.protcy.2013.12.159
Park, S.Y., Choi, J.H., Wang, S., and Park, S.S. (2006). "Design of a water quality monitoring network in a large river system using the genetic algorithm." Ecological Modelling, Vol. 199, No. 3, pp. 289-297. 10.1016/j.ecolmodel.2006.06.002
Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). ""Why should i trust you?" Explaining the predictions of any classifier." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, U.S., pp. 1135-1144. 10.18653/v1/N16-3020
Roh, S., and Park, D. (2021). "Sweet persimmons classification based on a mixed two-step synthetic neural network." Journal of Korea Multimedia Society, Vol. 24, No. 10, pp. 1358-1368.
Rosenblatt, F. (1958). "The perceptron: A probabilistic model for information storage and organization in the brain." Psychological review, Vol. 65, No. 6, pp. 386-408. 10.1037/h004251913602029
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
Sheng, T.Q., and Xu, Y.Z. (1993). "Distribution of dissolved oxygen and pH in Kuroshio area of East of China Sea." Marine Science Bulletin, Vol. 12, No. 4, pp. 55-62.
Wang, T.S., Tan, C.H., Chen, L., and Tsai, Y.C. (2008). "Applying artificial neural networks and remote sensing to estimate chlorophyll-a concentration in water body." Proceedings 2008 Second International Symposium on Intelligent Information Technology Application, IEEE, Shanghai, China, Vol. 1, pp. 540-544. 10.1109/IITA.2008.279
Wu, H., Huang, A., and Sutherland, J.W. (2022). "Layer-wise relevance propagation for interpreting LSTM-RNN decisions in predictive maintenance." The International Journal of Advanced Manufacturing Technology, Vol. 118, pp. 963-978. 10.1007/s00170-021-07911-9
Yoo, Y., Kim, D., and Lee, J. (2020). "Performance analysis of various activation functions in super resolution model." Proceedings of the Korea Information Processing Society Conference, Vol. 27, No. 1, pp. 504-507.
Zhou, T., Jiang, Z., Liu, X., and Tan, K. (2020). "Research on the long-term and short-term forecasts of navigable river's water-level fluctuation based on the adaptive multilayer perceptron." Journal of Hydrology, Vol. 591, 125285. 10.1016/j.jhydrol.2020.125285
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 56
  • No :11
  • Pages :801-813
  • Received Date : 2023-10-04
  • Revised Date : 2023-11-07
  • Accepted Date : 2023-11-07