應用雷達降雨資料與支撐向量機於洪水預報結果之修正研究

Translated title of the contribution: Study of Applying Radar Rainfall and Support Vector Machine to Correct Flood Forcasts

Jing Xue Wang, Yuan Chien Lin, Dong Sin Shih, Ray Shyan Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The weather surveillance radar (WSR) is used to locate precipitation which is more flexible in obtaining spatial and temporal variability than the Rain gauge. Thus, the study employs the rainfall data derived from Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS). By using the data, a flooding forecasting system is established by integrating the HEC-HMS with WASH123D. The Support Vector Machine (SVM) then modifies the errors in the estimation. The result shows that the water level variation can be estimated by the flooding forecasting system established by the Quantitative Precipitation Forecasting (QPF), however, the lack of precision remains. Thus, the SVM modifies the errors in order to improve the accuracy in terms of the water level observation. Overall, using the Weather Surveillance Radar (WSR) obtains more accurate simulation results than the Rain gauge. The correlation coefficient increases about 0.07 and reduces the root-mean-square error around 0.1 m. However, after the modification of SVM, the correlation coefficient increases about 0.08, and reduces the root-mean-square error around 0.09 m and peak-value-error about 0.2 m.

Translated title of the contributionStudy of Applying Radar Rainfall and Support Vector Machine to Correct Flood Forcasts
Original languageChinese (Traditional)
Pages (from-to)45-54
Number of pages10
JournalJournal of the Chinese Institute of Civil and Hydraulic Engineering
Volume33
Issue number1
DOIs
StatePublished - Mar 2021

Fingerprint Dive into the research topics of 'Study of Applying Radar Rainfall and Support Vector Machine to Correct Flood Forcasts'. Together they form a unique fingerprint.

Cite this