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http://dx.doi.org/10.21729/ksds.2022.15.2.45

Development of Return flow rate Prediction Algorithm with Data Variation based on LSTM  

Lee, Seung Yeon (Hongik University Research Institute of Science and Technology)
Yoo, Hyung Ju (Dept. of Civil Engineering, Hongik University)
Lee, Seung Oh (Dept. of Civil Engineering, Hongik University)
Publication Information
Journal of Korean Society of Disaster and Security / v.15, no.2, 2022 , pp. 45-56 More about this Journal
Abstract
The countermeasure for the shortage of water during dry season and drought period has not been considered with return flowrate in detail. In this study, the outflow of STP was predicted through a data-based machine learning model, LSTM. As the first step, outflow, inflow, precipitation and water elevation were utilized as input data, and the distribution of variance was additionally considered to improve the accuracy of the prediction. When considering the variability of the outflow data, the residual between the observed value and the distribution was assumed to be in the form of a complex trigonometric function and presented in the form of the optimal distribution of the outflow along with the theoretical probability distribution. It was apparently found that the degree of error was reduced when compared to the case not considering where the variance distribution. Therefore, it is expected that the outflow prediction model constructed in this study can be used as basic data for establishing an efficient river management system as more accurate prediction is possible.
Keywords
Return flowrate; Variability; Probability Distribution; LSTM;
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Times Cited By KSCI : 5  (Citation Analysis)
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