International Journal of Contents

  • 제14권4호
  • /
  • Pages.57-64
  • /
  • 2018
  • /
  • 1738-6764(pISSN)
  • /
  • 2093-7504(eISSN)
한국콘텐츠학회 (The Korea Contents Association)
권호 표지
DOI QR코드

DOI QR Code

Recurrent Neural Network with Multiple Hidden Layers for Water Level Forecasting near UNESCO World Heritage Site "Hahoe Village"

  • Oh, Sang-Hoon (Division of Information Communication Convergence Engineering, Mokwon University)
  • 투고 : 2018.08.02
  • 심사 : 2018.12.12
  • 발행 : 2018.12.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Among many UNESCO world heritage sites in Korea, "Historic Village: Hahoe" is adjacent to Nakdong River and it is imperative to monitor the water level near the village in a bid to forecast floods and prevent disasters resulting from floods.. In this paper, we propose a recurrent neural network with multiple hidden layers to predict the water level near the village. For training purposes on the proposed model, we adopt the sixth-order error function to improve learning for rare events as well as to prevent overspecialization to abundant events. Multiple hidden layers with recurrent and crosstalk links are helpful in acquiring the time dynamics of the relationship between rainfalls and water levels. In addition, we chose hidden nodes with linear rectifier activation functions for training on multiple hidden layers. Through simulations, we verified that the proposed model precisely predicts the water level with high peaks during the rainy season and attains better performance than the conventional multi-layer perceptron.

키워드

참고문헌

  1. K. Hornik, M. Stinchcombe, and H. White, "Multilayer Feed-forward Networks are Universal Approximators," Neural Networks, vol. 2, 1989, pp. 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  2. K. Hornik, "Approximation Capabilities of Multilayer Feedforward Networks," Neural Networks, vol. 4, 1991, pp. 251-257. https://doi.org/10.1016/0893-6080(91)90009-T
  3. S. Suzuki, "Constructive Function Approximation by Three-Layer Artificial Neural Networks," Neural Networks, vol. 11, 1998, pp. 1049-1058. https://doi.org/10.1016/S0893-6080(98)00068-9
  4. C. Phua, D. Alahakoon, and V. Lee, "Minority report in fraud detection: classification of skewed data," ACM SIGKDD Explorations Newsletter, vol. 6, issue. 1, 2004, pp. 50-59. https://doi.org/10.1145/1007730.1007738
  5. C. M. Bishop, Pattern Recognition and Machine Learning, Springer Science+Business Media LLC, 2006.
  6. F. Altiparmak, B. Dengiz, and A. E. Smith, "A general neural network model for estimating telecommunications network reliability," IEEE Trans. Reliability, vol. 58, issue. 1, 2009, pp. 2-9. https://doi.org/10.1109/TR.2008.2011854
  7. I. Kaaastra and M. Boyd, "Designing a neural network for forecasting financial and economic time series," Neurocomputing, vol. 10, issue. 3, 1996, pp. 215-236. https://doi.org/10.1016/0925-2312(95)00039-9
  8. Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, 2015, pp. 436-444. https://doi.org/10.1038/nature14539
  9. T. L. Lee, "Back-propagation neural networks for longterm tidal predictions," Ocean Engineering, vol. 31, 2004, pp. 225-238. https://doi.org/10.1016/S0029-8018(03)00115-X
  10. T. L. Lee, "Back-propagation neural network for the prediction of the short-term storm-surge in Taichung harbor, Taiwan," Engineering Applications of Artificial Intelligence, vol. 21, 2008, pp. 63-72. https://doi.org/10.1016/j.engappai.2007.03.002
  11. T. L. Lee, "Predictions of typhoon storm surge in Taiwan using artificial neural networks," Advances in Engineering Software, vol. 40, 2009, pp. 1200-1206. https://doi.org/10.1016/j.advengsoft.2007.06.005
  12. S. Lee and D. G. Evangelista, "Earthquake-induced landslide-susceptibility mapping using an artificial neural network," Natural Hazards and Earth System Sciences, vol. 6, 2006, pp. 687-695. https://doi.org/10.5194/nhess-6-687-2006
  13. B. Pradhan and S. Lee, "Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models," Environ Earth Sci., vol. 60, 2010, pp. 1037-1054. https://doi.org/10.1007/s12665-009-0245-8
  14. H. L. Cloke and F. Pappenberger, "Ensemble flood forecasting: A review," Journal of Hydrology, vol. 375, 2009, pp. 613-626. https://doi.org/10.1016/j.jhydrol.2009.06.005
  15. L. H. Feng and J. Lu, "The practical research on flood forecasting based on artificial neural networks," Expert Systems with Applications, vol. 37, 2010, pp. 2974-2977. https://doi.org/10.1016/j.eswa.2009.09.037
  16. D. Demeritt, H. Cloke, F. Pappenberger, J. Thienlen, J. Bartholmes, and M. H. Ramos, "Ensemble predictions and perceptions of risks, uncertainty, and error in flood forecasting," Environmental Hazards, vol. 7, no. 2, 2007, pp.115-127. https://doi.org/10.1016/j.envhaz.2007.05.001
  17. A. de Roo, B. Gouweleeuw, J. Thielen, J. Bartholmes, P. Bongioannini-Cerlini, E. Todini, P. D. Bates, M. Horritt, N. Hunter, K. Beven, F. Pappenberger, E. Heise, G. Rivin, M. Hils, A. Hollingsworth, B. Holst, J. Kwadijk, P. Reggiani, M. Van Dijk, K. Sattler, and E. Sprokkereef, "Development of a European flood forecasting system," International Journal of River Basin Management, vol. 1, 2003, pp. 49-59. https://doi.org/10.1080/15715124.2003.9635192
  18. C. W. Dawson and R. L. Wilby, "Hydrological modeling using artificial neural networks," Progress in Physical Geography, vol. 25, 2001, pp. 80-108. https://doi.org/10.1177/030913330102500104
  19. A. Atiya and S. Shaheen, "A comparison between neuralnetwork forecasting techniques-case study: river flow forecasting," IEEE Trans. Neural Networks, vol. 10, no. 2, 1999, pp. 402-409. https://doi.org/10.1109/72.750569
  20. C. W. Dawson, R. J. Abrahart, A. Y. Shamseldin, and R. L. Wilby, "Flood estimation at ungauged sites using artificial neural networks," Journal of Hydrology, vol. 319, 2006, pp. 391-409. https://doi.org/10.1016/j.jhydrol.2005.07.032
  21. Y. Wei, W. Xu, Y. Fan, and H.-T. Tasi, "Artificial neural network based predictive method for flood disaster," Computers & Industrial Engineering, vol. 42, 2002, pp. 383-390. https://doi.org/10.1016/S0360-8352(02)00047-5
  22. M. P. Rajurkar, U. C. Kothyari, and U. C. Chaube, "Modelling of the daily rainfall-runoff relationship with artificial neural network," Journal of Hydrology, vol. 285, 2004, pp. 96-113. https://doi.org/10.1016/j.jhydrol.2003.08.011
  23. S. Raid, J. Mania, L. Bouchaou, and Y. Najjar, "Rainfall-runoff model using an artificial neural network approach," Mathematical and Computer Modelling, vol. 40, 2004, pp. 839-846. https://doi.org/10.1016/j.mcm.2004.10.012
  24. S. Raid, J. Mania, L. Bouchaou, and Y. Najjar, "Predicting catchment flow in a semi-arid region via an artificial neural network," Hydrological Processes, vol. 18, 2004, pp. 2387-2393. https://doi.org/10.1002/hyp.1469
  25. L. H. C. Chua and T. S. W. Wong, "Improving event-based rainfall-runoff modeling using a combined artificial neural network-kinematic wave approach," Journal of Hydrology, vol. 390, 2010, pp. 92-107. https://doi.org/10.1016/j.jhydrol.2010.06.037
  26. S. H. Oh and H. Wakuya, "Hydrological modeling of water level near Hahoe Village based on multi-layer perceptron," International Journal of Contents, vol. 12, 2016, pp. 49-53.
  27. D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing, Cambridge, MA, 1986.
  28. F. J. Chang, P. A. Chen, Y. R. Ku, E. Huang, and K. Y. Chang, "Real-time multi-step ahead water level forecasting by recurrent neural networks for urban flood control," Journal of Hydrology, vol. 517, 2014, pp.836-846. https://doi.org/10.1016/j.jhydrol.2014.06.013
  29. S. H. Oh, "Error back-propagation algorithm for classification of imbalanced data," Neurocomputing, vol. 74, 2011, pp. 1058-1061. https://doi.org/10.1016/j.neucom.2010.11.024
  30. S. H. Oh, "Improving the error back-propagation algorithm with a modified error function," IEEE Trans. Neural Networks, vol.8, 1997, pp. 799-803. https://doi.org/10.1109/72.572117
  31. S. H. Oh, "Improving the water level prediction of multi-layer perceptron with a modified error function," Internation Journal of Contents, vol. 13, 2017, pp. 23-28.
  32. X. Glorot, A. Bordes, and Y. Bengio, "Deep sparse rectifier neural networks," Proc. The 14th AISTATS, Fort Lauderdale, FL, USA, vol. 15, 2011, pp. 315-323.