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http://dx.doi.org/10.11108/kagis.2017.20.3.012

Development and Performance Assessment of the Nakdong River Real-Time Runoff Analysis System Using Distributed Model and Cloud Service  

KIM, Gil-Ho (Department of Hydro Science and Engineering Research, KICT)
CHOI, Yun-Seok (Department of Hydro Science and Engineering Research, KICT)
WON, Young-Jin (Research Institute of HermeSys Co. Ltd.)
KIM, Kyung-Tak (Department of Hydro Science and Engineering Research, KICT)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.20, no.3, 2017 , pp. 12-26 More about this Journal
Abstract
The objective of this study was to develop a runoff analysis system of the Nakdong River watershed using the GRM (Grid-based Rainfall-runoff Model), a physically-based distributed rainfall-runoff model, and to assess the system run time performance according to Microsoft Azure VM (Virtual Machine) settings. Nakdong River watershed was divided into 20 sub-watersheds, and GRM model was constructed for each subwatershed. Runoff analysis of each watershed was calculated in separated CPU process that maintained the upstream and downstream topology. MoLIT (Ministry of Land, Infrastructure and Transport) real-time radar rainfall and dam discharge data were applied to the analysis. Runoff analysis system was run in Azure environment, and simulation results were displayed through web page. Based on this study, the Nakdong River real-time runoff analysis system, which consisted of a real-time data server, calculation node (Azure), and user PC, could be developed. The system performance was more dependent on the CPU than RAM. Disk I/O and calculation bottlenecks could be resolved by distributing disk I/O and calculation processes, respectively, and simulation runtime could thereby be decreased. The study results could be referenced to construct a large watershed runoff analysis system using a distributed model with high resolution spatial and hydrological data.
Keywords
Distributed Model; Cloud Service; Real Time Runoff Analysis System; Flood; GRM(Grid based Rainfall-runoff Model); Azure;
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Times Cited By KSCI : 9  (Citation Analysis)
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