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An architecture consisting of a serial coupling of the one-dimensionalconvolutional neural network (1D-CNN) and the long short-term memory (LSTM)network, which is referred as CNNsLSTM, was proposed for hourly-scalerainfall-runoff modeling in this study. In CNNsLTSM, the CNN component receivesthe hourly meteorological time series data for a long duration, and then theLSTM component receives the extracted features from 1D-CNN and the hourlymeteorological time series data for a short-duration. As a case study, CNNsLSTMwas implemented for hourly rainfall-runoff modeling at the Ishikari Riverwatershed, Japan. The meteorological dataset, consists of precipitation, airtemperature, evapotranspiration, and long- and short-wave radiation, wereutilized as input, and the river flow was used as the target data. To evaluatethe performance of proposed CNNsLSTM, results of CNNsLSTM were compared withthose of 1D-CNN, LSTM only with hourly inputs (LSTMwHour), parallelarchitecture of 1D-CNN and LSTM (CNNpLSTM), and the LSTM architecture whichuses both daily and hourly input data (LSTMwDpH). CNNsLSTM showed clearimprovements on the estimation accuracy compared to the three conventionalarchitectures (1D-CNN, LSTMwHour, and CNNpLSTM), and recently proposedLSTMwDpH. In comparison to observed flows, the median of the NSE values for thetest period are 0.455-0.469 for 1D-CNN (based on NCHF=8, 16, and 32, thenumbers of the channels of the feature map of the first layer of CNN),0.639-0.656 for CNNpLSTM (based on NCHF=8, 16, and 32), 0.745 for LSTMwHour,0.831 for LSTMwDpH, and 0.865-0.873 for CNNsLSTM (based on NCHF=8, 16, and 32).Furthermore, the proposed CNNsLSTM reduces the median RMSE of 1D-CNN by50.2%-51.4%, CNNpLSTM by 37.4%-40.8%, LSTMwHour by 27.3%-29.5%, and LSTMwDpH by10.6%-13.4%.

Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling