Browsing by Author "Wei, Hsiao-Ping"

Now showing 1 - 6 of 6

An improved genetic programming to SSM/I estimation typhoon precipitation over ocean
(2011-07-30)
Chen, Li
;
Yeh, Keh-Chia
;
Wei, Hsiao-Ping
;
Liu, Gin-Rong
;
土木工程學系
This article proposes an improved multi-run genetic programming (GP) and applies it to estimate the typhoon rainfall over ocean using multi-variable meteorological satellite data. GP is a well-known evolutionary programming and data mining method used to automatically discover the complex relationships among nonlinear systems. The main advantage of GP is to optimize appropriate types of function and their associated coefficients simultaneously. However, the searching efficiency of traditional GP can be decreased by the complex structure of parse tree to represent the multiple input variables. This study processed an improvement to enhance escape ability from local optimums during the optimization procedure. We continuously run GP several times by replacing the terminal nodes at the next run with the best solution at the current run. The current method improves GP, obtaining a highly nonlinear meaningful equation to estimate the rainfall. In the case study, this improved GP (IGP) described above combined with special sensor microwave imager (SSM/I) seven channels was employed. These results are then verified with the data from four offshore rainfall stations located on islands around Taiwan. The results show that the IGP generates sophisticated and accurate multi-variable equation through two runs. The performance of IGP outperforms the traditional multiple linear regression, back-propagated network (BPN) and three empirical equations. Because the extremely high values of precipitation rate are quite few and the number of zero values (no rain) is very large, the underestimations of heavy rainfall are obvious. A simple genetic algorithm was therefore used to search for the optimal threshold value of SSM/I channels, detecting the data of no rain. The IGP with two runs, used to construct an appropriate mathematical function to estimate the precipitation, can obtain more favourable results from estimating extremely high values. Copyright. (C) 2011 John Wiley & Sons, Ltd.
Combining satellite data for estimation of rainfall for analysis of a watershed scale
(2011)
Wei, Hsiao-Ping
;
Yeh, Keh-Chia
;
Liu, Gin-Rong
;
Chao, Chun-Chieh
;
土木工程學系
The microwave/infrared rainfall algorithm (MIRA) method combines microwave and infrared channels for their respective contributions to the achievement of higher rainfall-correlated and spatial resolution data, in order to acquire appropriate rainfall data for hydrological models. In this study, estimates of rainfall rate using the MIRA method are affected by the time lag between the data acquisition of the microwave and infrared channels. The root-mean-square error (RMSE) for comparing the rainfall rate retrieved by the MIRA method (5-minute time lag between microwave and infrared data) to the ground rainfall rate in Taiwan is 12.23 mm hour(-1) with a correlation coefficient of 0.58. In addition, by comparing rainfall estimated by the TMI-2A12 and the MIRA method for three typhoons, the rainfall estimated by the latter method is more significantly correlated with the ground-observed rainfall.
Estimating the Risk of River Flow under Climate Change in the Tsengwen River Basin
(2016-03-01)
Wei, Hsiao-Ping
;
Yeh, Keh-Chia
;
Liou, Jun-Jih
;
Chen, Yung-Ming
;
Cheng, Chao-Tzuen
This study evaluated the overflow risk of the Tsengwen River under a climate change scenario by using bias-corrected dynamic downscaled data as inputs for a SOBEK model (Deltares, the Netherlands). The results showed that the simulated river flow rate at Yufeng Bridge (upstream), Erxi Bridge (midstream), and XinZong (1) (downstream) stations are at risk of exceeding the management plan's flow rate for three projection periods (1979-2003, 2015-2039, 2075-2099). After validation with the geomorphic and hydrological data collected in this study, the frequency at which the flow rate exceeded the design flood was 2 in 88 events in the base period (1979-2003), 6 in 82 events in the near future (2015-2039), and 10 in 81 events at the end of the century (2075-2099).
Estimating Typhoon Rainfall over Sea from SSM/I Satellite Data Using a Back-propagated Network
(2010-01-01)
Chine, Wen-Huan
;
Wei, Hsiao-Ping
;
Yeh, Keh-Chia
;
Chen, Li
;
Liu, Gin-Rong
This paper proposes a back-propagated network (BPN) and applies it to predict the rainfall using meteorological satellite data. In the case study, the back-propagated network (BPN) combining with SGji-Dao SM/I microwave frequency channels was employed to establish a suitable method for estimating rainfall at sea surface during typhoon periods. These results are then verified with the data from four rainfall stations located at Peng-Jia-Yu, Don-, Lan-Yu, and Green Island. From the results, the back-propagated network (BPN) outperforms the traditional multiple linear regression and empirical equations with lower estimating errors for predicting the sea-surface rainfall during typhoon periods.
Levee Overtopping Risk Assessment under Climate Change Scenario in Kao-Ping River, Taiwan
(2020-06-01)
Wei, Hsiao-Ping
;
Su, Yuan-Fong
;
Cheng, Chao-Tzuen
;
Yeh, Keh-Chia
;
土木工程學系
With the growing concern about the failure risk of river embankments in a rapidly changing climate, this study aims to quantify the overtopping probability of river embankment in Kao-Ping River basin in southern Taiwan. A water level simulation model is calibrated and validated with historical typhoon events and the calibrated model is further used to assess overtopping risk in the future under a climate change scenario. A dynamic downscaled projection dataset, provided by Meteorological Research Institute (MRI) has been further downscaled to 5-km grids and bias-corrected with a quantile mapping method, is used to simulate the water level of Kao-Ping River in the future. Our results highlighted that the overtopping risk of Kao-Ping River increased by a factor of 5.7 similar to 8.0 by the end of the 21st century.
Using Structural Measures to Reduce Flood Losses in a Future Extreme Weather Event
(2016-10)
Wei, Hsiao-Ping
;
Li, Hsin-Chi
;
Yeh, Keh-Chia
;
Liou, Jun-Jih
;
Chen, Yung-Ming
Climate change is a real threat that brings heavier and more frequent extreme weather events. Many researchers have found that climate change will increase the risk of flooding around the world. The SOBEK inundation model and Taiwan Typhoon Loss Assessment System (TLAS) are used in this study to see if embankment and flood storage control measures can mitigate the impact (loss) of extreme events under climate change. Based on the simulation results, three inundation cases are simulated starting about 18 hr after the embankment is breached. When embankment and detention pond measures are not in place (Case A), the average flood depth in the catchment area caused by the TOP event is roughly 1.45 m. When adaptation measures such as [Case B raising embankments (Case B) and] are in place, the flood depth decreased by 39 and 33%, respectively. TLAS was used to evaluate the potential losses associated with the three cases (Case A, B, and C). When we add flood-control measures, the total loss reduced by adaptation measures for future extreme events in the Zhengwen River basin was NT$7 billion. This study provides applicable coping strategies for future extreme precipitation events that will effectively reduce the impact of extreme storm events.