PROGRESS IN GEOGRAPHY ›› 2020, Vol. 39 ›› Issue (4): 636-642.doi: 10.18306/dlkxjz.2020.04.010

• Special Column: Utilization of Floodwater Resource • Previous Articles     Next Articles

Daily runoff predication using LSTM at the Ankang Station, Hanjing River

HU Qingfang1, CAO Shiyi1, YANG Huibin1,2, WANG Yintang1, LI Linjie1, WANG Lihui2   

  1. 1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
    2. Department of Water Resources, Hydropower and PortEngineering, Fuzhou University, Fuzhou 350116, China
  • Received:2019-11-04 Revised:2020-02-20 Online:2020-04-28 Published:2020-06-28
  • Supported by:
    National Key Research and Development Program of China(2016YFC0400902);National Natural Science Foundation of China(51479118)


Based on the hydrological data from 2003 to 2014, Long-Short Term Memory (LSTM) was used to construct a daily runoff prediction model for the Ankang discharge station in the upper reaches of the Hanjiang River. The accuracy of daily runoff prediction was evaluated under different input conditions. The result shows that when the foreseeing period is one day, the efficiency coefficient of the LSTM in the calibration period and the validation period can reach 0.68 and 0.74 respectively under the condition that only the previous daily runoff of the Ankang Station is used as input. When the previous areal rainfall of the catchment and the previous daily runoff of the upstream Shiquan Station were added to the LSTM model as input variables, the daily runoff prediction precision was improved. The efficiency coefficient of the training period and the validation period could reach 0.83 and 0.84, respectively. The root mean square error was also significantly reduced. The accuracy of the main flood peak flow forecasting also increased. The LSTM can effectively avoid the problem of over-fitting, and has better generalization performance. However, when the foreseeable period is extended from one day to two or three days, the performance of LSTM is significantly reduced.

Key words: long-short term memory, daily runoff forecast, Hanjiang River Basin, Ankang Station