Floods are often caused by short-term heavy rainfall. An Integrated Flood Analysis System (IFAS) model is good at runoff simulation and a Long Short-Term Memory (LSTM) model is good at learning massive data and realizing rainfall forecast. In this paper, the applicability of the IFAS model to runoff simulation in the Tokachi River basin and the LSTM model to forecast hourly rainfall was studied, and the accuracy of flood prediction was also studied by inputting the optimal rainfall data forecasted by the LSTM model into the IFAS model. The research results show that the IFAS model can accurately simulate the runoff process in the Tokachi River basin. In the calibration period and the verification period, the Nash–Sutcliffe Efficiency coefficient (NSE) of all simulation results are above 0.75; the LSTM model can achieve forecast hourly rainfall with high precision, the NSE of best forecast results is 0.86; the IFAS model can achieve flood prediction with high precision by using the optimal rainfall data forecasted by the LSTM model, the NSE of simulation result is 0.81. The above conclusions show that it is of great significance to combine the hourly rainfall forecasted by the LSTM model with the IFAS model for flood prediction.

洪水通常是由短期强降雨引起的。综合洪水分析系统（IFAS）模型擅长径流模拟，长短期记忆（LSTM）模型擅长学习海量数据并实现降雨预报。本文研究了IFAS模型在十胜河流域径流模拟和LSTM模型预测每小时降雨量的适用性，并通过将LSTM模型预测的最佳降雨数据输入到LSTM模型中，研究洪水预测的准确性。 IFAS 模型。研究结果表明，IFAS模型能够准确模拟十胜河流域的径流过程。在校准期和验证期，所有模拟结果的纳什-萨特克利夫效率系数（NSE）均在0.75以上；LSTM模型可以实现高精度的每小时降雨预报，最佳预报结果的NSE为0.86；IFAS模型利用LSTM模型预测的最优降雨数据可以实现高精度的洪水预测，模拟结果的NSE为0.81。以上结论表明，将LSTM模型预测的每小时降雨量与IFAS模型相结合进行洪水预测具有重要意义。