Understanding the Way Machines Simulate Hydrological Processes-A Case Study of Predicting Fine-Scale Watershed Response on a Distributed Framework
- Authors
- Kim, Dongkyun; Lee, Yong Oh; Jun, Changhyun; Kang, Seokkoo
- Issue Date
- Jun-2023
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Deep learning; distributed hydrologic model; hydrology; long short-term memory (LSTM); machine learning; radar precipitation
- Citation
- IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, v.61, pp.1 - 18
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Volume
- 61
- Start Page
- 1
- End Page
- 18
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/189423
- DOI
- 10.1109/TGRS.2023.3285540
- ISSN
- 0196-2892
- 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.
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