Flood prediction in the Namgang Dam basin using a long short-term memory (LSTM) algorithm

  • Lee, Seungsoo (Division for Integrated Water Management, Korea Environmental Institute) ;
  • An, Hyunuk (Department of Agricultural and Rural Engineering, Chungnam National University) ;
  • Hur, Youngteck (K-water Research Institute) ;
  • Kim, Yeonsu (K-water Research Institute) ;
  • Byun, Jisun (Department of Civil Engineering, Chungnam National University)
  • Received : 2020.04.23
  • Accepted : 2020.07.14
  • Published : 2020.09.01


Flood prediction is an important issue to prevent damages by flood inundation caused by increasing high-intensity rainfall with climate change. In recent years, machine learning algorithms have been receiving attention in many scientific fields including hydrology, water resources, natural hazards, etc. The performance of a machine learning algorithm was investigated to predict the water elevation of a river in this study. The aim of this study was to develop a new method for securing a large enough lead time for flood defenses by predicting river water elevation using the a long- short-term memory (LSTM) technique. The water elevation data at the Oisong gauging station were selected to evaluate its applicability. The test data were the water elevation data measured by K-water from 15 February 2013 to 26 August 2018, approximately 5 years 6 months, at 1 hour intervals. To investigate the predictability of the data in terms of the data characteristics and the lead time of the prediction data, the data were divided into the same interval data (group-A) and time average data (group-B) set. Next, the predictability was evaluated by constructing a total of 36 cases. Based on the results, group-A had a more stable water elevation prediction skill compared to group-B with a lead time from 1 to 6 h. Thus, the LSTM technique using only measured water elevation data can be used for securing the appropriate lead time for flood defense in a river.


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