Abstract:
This study developed a deep neural network (DNN)-based distributed hydrologic model for an urban watershed in the Republic of Korea. The developed model is composed of mu...Show MoreMetadata
Abstract:
This study developed a deep neural network (DNN)-based distributed hydrologic model for an urban watershed in the Republic of Korea. The developed model is composed of multiple long short-term memory (LSTM) hidden units connected by a fully connected layer. To examine the study area using the developed model, time series of 10-min radar-gauge composite precipitation data and 10-min temperature data at 239 model grid cells with 1-km resolution is used as inputs to simulate 10-min watershed flow discharge as an output. The model performed well for the calibration period (2013–2016) and the validation period (2017–2019), with Nash–Sutcliffe efficiency coefficient values being 0.99 and 0.67, respectively. Further in-depth analyses were performed to derive the following conclusions: 1) the map of runoff–precipitation ratios produced using the developed DNN model resembled imperviousness ratio map of the study area from the land cover data, revealing that the DNN successfully deep-learned the precipitation partitioning processes only with the input and output data without depending on any priori information about hydrology; 2) the model successfully reproduced the soil moisture-dependent runoff process, an essential prerequisite of continuous hydrologic models; and 3) each LSTM unit has a different temporal sensitivity to the precipitation stimulus, with fast-response LSTM units having greater output weight factors near the watershed outlet, which implies that the developed model has a mechanism to separately consider the hydrological components with distinct response time such as direct runoff and the groundwater-driven baseflow.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 61)