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Article

Determination of Deep Learning Model and Optimum Length of Training Data in the River with Large Fluctuations in Flow Rates

1
Emergency Management Institute, Kyungpook National University, Sangju 37224, Gyeongbuk, Korea
2
Department of Advanced Science and Technology Convergence, Kyungpook National University, Sangju 37224, Gyeongbuk, Korea
3
Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Gyeonggi-do, Korea
4
Korea Research Institute for Construction Policy, Seoul 07071, Korea
*
Author to whom correspondence should be addressed.
Water 2020, 12(12), 3537; https://doi.org/10.3390/w12123537
Received: 10 November 2020 / Revised: 9 December 2020 / Accepted: 13 December 2020 / Published: 16 December 2020
(This article belongs to the Section Urban Water Management)
Recently, developing countries have steadily been pushing for the construction of stream-oriented smart cities, breaking away from the existing old-town-centered development in the past. Due to the accelerating effects of climate change along with such urbanization, it is imperative for urban rivers to establish a flood warning system that can predict the amount of high flow rates of accuracy in engineering, compared to using the existing Computational Fluid Dynamics (CFD) models for disaster prevention. In this study, in the case of streams where missing data existed or only small observations were obtained, the variation in flow rates could be predicted with only the appropriate deep learning models, using only limited time series flow data. In addition, the selected deep learning model allowed the minimum number of input learning data to be determined. In this study, the time series flow rates were predicted by applying the deep learning models to the Han River, which is a highly urbanized stream that flows through the capital of Korea, Seoul and has a large seasonal variation in the flow rate. The deep learning models used are Convolution Neural Network (CNN), Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Gated Recurrent Unit (GRU). Sequence lengths for time series runoff data were determined first to assess the accuracy and applicability of the deep learning models. By analyzing the forecast results of the outflow data of the Han River, sequence length for 14 days was appropriate in terms of the predicted accuracy of the model. In addition, the GRU model is effective for deep learning models that use time series data of the region with large fluctuations in flow rates, such as the Han River. Furthermore, through this study, it was possible to propose the minimum number of training data that could provide flood warning system with an effective flood forecasting system although the number of input data such as flow rates secured in new towns developed around rivers was insufficient. View Full-Text
Keywords: flood warning system; deep learning model; time-series flow rates; large seasonal variation; Simple RNN; CNN; LSTM; Bidirectional LSTM; GRU; sequence length; minimum number of training data flood warning system; deep learning model; time-series flow rates; large seasonal variation; Simple RNN; CNN; LSTM; Bidirectional LSTM; GRU; sequence length; minimum number of training data
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MDPI and ACS Style

Park, K.; Jung, Y.; Kim, K.; Park, S.K. Determination of Deep Learning Model and Optimum Length of Training Data in the River with Large Fluctuations in Flow Rates. Water 2020, 12, 3537. https://doi.org/10.3390/w12123537

AMA Style

Park K, Jung Y, Kim K, Park SK. Determination of Deep Learning Model and Optimum Length of Training Data in the River with Large Fluctuations in Flow Rates. Water. 2020; 12(12):3537. https://doi.org/10.3390/w12123537

Chicago/Turabian Style

Park, Kidoo, Younghun Jung, Kyungtak Kim, and Seung K. Park 2020. "Determination of Deep Learning Model and Optimum Length of Training Data in the River with Large Fluctuations in Flow Rates" Water 12, no. 12: 3537. https://doi.org/10.3390/w12123537

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