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 contribution||Study of Applying Radar Rainfall and Support Vector Machine to Correct Flood Forcasts|
|Original language||Chinese (Traditional)|
|Number of pages||10|
|Journal||Journal of the Chinese Institute of Civil and Hydraulic Engineering|
|State||Published - Mar 2021|