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A Study on the Data Driven Neural Network Model for the Prediction of Time Series Data: Application of Water Surface Elevation Forecasting in Hangang River Bridge

시계열 자료의 예측을 위한 자료 기반 신경망 모델에 관한 연구: 한강대교 수위예측 적용

  • Yoo, Hyungju (Dept. of Civil Engineering, Hongik Univ.) ;
  • Lee, Seung Oh (Dept. of Civil Engineering, Hongik Univ.) ;
  • Choi, Seohye (Korea Institute of Civil Engineering and Building technology) ;
  • Park, Moonhyung (Korea Institute of Civil Engineering and Building technology)
  • 유형주 (홍익대학교 토목공학과) ;
  • 이승오 (홍익대학교 토목공학과) ;
  • 최서혜 (한국건설기술연구원 국토연구보전 본부) ;
  • 박문형 (한국건설기술연구원 국토연구보전 본부)
  • Received : 2019.05.27
  • Accepted : 2019.06.20
  • Published : 2019.06.30

Abstract

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

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Fig. 1. Structure of ANN model

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Fig. 2. Structure of NARX model

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Fig. 3. The tidal level at Incheon and water surface elevation at Hangang RIver Bridge in July 2011

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Fig. 4. Discrete fourier transform of water surface elevation, tidal level and precipitation in July 2011

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Fig. 5. Hidden layer sensitivity analysis result

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Fig. 6. Time delay sensitivity analysis result

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Fig. 7. The water surface elevation distribution (left) and scatter plot (right) according to neural network model

Table 1. Input data characteristics

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Table 2. Model setup

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Table 3. Comparison of accuracy about ANN, RNN, and NARX model

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Acknowledgement

Supported by : Korea Agency for Infrastructure Technology Advancement(KAIA)

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