Understanding the Way Machines Simulate Hydrological Processes - A Case Study of Predicting Fine-scale Watershed Response on a Distributed Framework
DC Field | Value | Language |
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dc.contributor.author | Kim, D. | - |
dc.contributor.author | Lee, Y.O. | - |
dc.contributor.author | Jun, C. | - |
dc.contributor.author | Kang, S. | - |
dc.date.accessioned | 2023-12-11T07:30:44Z | - |
dc.date.available | 2023-12-11T07:30:44Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 0196-2892 | - |
dc.identifier.issn | 1558-0644 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32086 | - |
dc.description.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 | - |
dc.format.extent | 1 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Understanding the Way Machines Simulate Hydrological Processes - A Case Study of Predicting Fine-scale Watershed Response on a Distributed Framework | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/TGRS.2023.3285540 | - |
dc.identifier.scopusid | 2-s2.0-85162692212 | - |
dc.identifier.wosid | 001022708100003 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Geoscience and Remote Sensing, v.61, pp 1 - 1 | - |
dc.citation.title | IEEE Transactions on Geoscience and Remote Sensing | - |
dc.citation.volume | 61 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 1 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Geochemistry & Geophysics | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Remote Sensing | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Geochemistry & Geophysics | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Remote Sensing | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.subject.keywordPlus | RADAR RAINFALL | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | STREAMFLOW | - |
dc.subject.keywordPlus | PRECIPITATION | - |
dc.subject.keywordPlus | ASSIMILATION | - |
dc.subject.keywordPlus | CALIBRATION | - |
dc.subject.keywordPlus | ACCURACY | - |
dc.subject.keywordAuthor | <italic xmlns:ali=http://www.niso.org/schemas/ali/1.0/ xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink xmlns:xsi=http://www.w3.org/2001/XMLSchema-instance>Deep Learning</italic> | - |
dc.subject.keywordAuthor | <italic xmlns:ali=http://www.niso.org/schemas/ali/1.0/ xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink xmlns:xsi=http://www.w3.org/2001/XMLSchema-instance>Distributed Hydrologic Model</italic> | - |
dc.subject.keywordAuthor | <italic xmlns:ali=http://www.niso.org/schemas/ali/1.0/ xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink xmlns:xsi=http://www.w3.org/2001/XMLSchema-instance>Hydrology</italic> | - |
dc.subject.keywordAuthor | <italic xmlns:ali=http://www.niso.org/schemas/ali/1.0/ xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink xmlns:xsi=http://www.w3.org/2001/XMLSchema-instance>LSTM</italic> | - |
dc.subject.keywordAuthor | <italic xmlns:ali=http://www.niso.org/schemas/ali/1.0/ xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink xmlns:xsi=http://www.w3.org/2001/XMLSchema-instance>Machine Learning</italic> | - |
dc.subject.keywordAuthor | <italic xmlns:ali=http://www.niso.org/schemas/ali/1.0/ xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink xmlns:xsi=http://www.w3.org/2001/XMLSchema-instance>Radar Precipitation</italic> | - |
dc.subject.keywordAuthor | Computational modeling | - |
dc.subject.keywordAuthor | Data models | - |
dc.subject.keywordAuthor | Discharges (electric) | - |
dc.subject.keywordAuthor | Distributed databases | - |
dc.subject.keywordAuthor | Precipitation | - |
dc.subject.keywordAuthor | Time series analysis | - |
dc.subject.keywordAuthor | Watersheds | - |
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