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http://dx.doi.org/10.5762/KAIS.2021.22.4.287

Evaluation of LSTM Model for Inflow Prediction of Lake Sapgye  

Hwang, Byung-Gi (Department of Civil Engineering, Sangmyung University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.4, 2021 , pp. 287-294 More about this Journal
Abstract
A Python-based LSTM model was constructed using a Tensorflow backend to estimate the amount of outflow during floods in the Gokgyo-cheon basin flowing into the Sapgyo Lake. To understand the effects of the length of input data used for learning, i.e., the sequence length, on the performance of the model, the model was implemented by increasing the sequence length to three, five, and seven hours. Consequently, when the sequence length was three hours, the prediction performance was excellent over the entire period. As a result of predicting three extreme rainfall events in the model verification, it was confirmed that an average NSE of 0.96 or higher was obtained for one hour in the leading time, and the accuracy decreased gradually for more than two hours in the leading time. In conclusion, the flood level at the Gangcheong station of Gokgyo-cheon can be predicted with high accuracy if the prediction is performed for one hour of leading time with a sequence length of three hours.
Keywords
Python; LSTM; Tensorflow; Sequence length; leading time;
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