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
- 2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Computational modeling; Data models; Discharges (electric); Distributed databases; Precipitation; Time series analysis; Watersheds
- Citation
- IEEE Transactions on Geoscience and Remote Sensing, v.61, pp 1 - 1
- Pages
- 1
- Journal Title
- IEEE Transactions on Geoscience and Remote Sensing
- Volume
- 61
- Start Page
- 1
- End Page
- 1
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/69761
- DOI
- 10.1109/TGRS.2023.3285540
- ISSN
- 0196-2892
1558-0644
- Abstract
- This study developed a Deep Neural Network (DNN) based distributed hydrologic model for an urban watershed in 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-minute radar-gauge composite precipitation data and 10-minute temperature data at 239 model grid cells with 1km resolution were used as inputs to simulate 10-minute watershed flow discharge as an output. The model performed well for the calibration period (2013-2016) and 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; (3) each LSTM unit has 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. IEEE
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.