2022
DOI: 10.3390/w14121866
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Real-Time Flood Warning System Application

Abstract: The reliability of weather radar data in real-time flood forecasting and early warning system remain ambivalent due to high uncertainty in Quantitative Precipitation Forecasts (QPF). In this study, a methodology is presented with the objective to improve the flood forecasting results with the application of radar rainfall calculated in three different ways. The QPF radar rainfall forecast data of four typhoon events in Fèngshān River Basin, Taiwan, were simulated using the WASH123D numerical model. The simulat… Show more

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Cited by 5 publications
(4 citation statements)
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“…In Wu et al [28], a physics-based model was implemented to forecast flooding for 1 h ahead in Taiwan. The authors managed to improve their results by 80% when compared with direct simulated values of the river using real-time data correction.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…In Wu et al [28], a physics-based model was implemented to forecast flooding for 1 h ahead in Taiwan. The authors managed to improve their results by 80% when compared with direct simulated values of the river using real-time data correction.…”
Section: Discussionmentioning
confidence: 99%
“…Hydrological models rely on physics-based equations to determine future flood occurrences. Even though this approach achieved good results in previous studies [25][26][27][28][29], it has limitations regarding its complex modeling, computational cost, the need for many hydro-geomorphic input attributes, and high precision of mapping attributes, which may result in large errors [10,19,[30][31][32].…”
Section: Introductionmentioning
confidence: 99%
“…Studies have shown that this expenditure can be reduced by 80%, if only 20% of this cost is spent on practical disaster-response activities [3]. Therefore, flood warning systems are introduced to minimize the impact of loss caused by flooding events [4][5][6][7][8][9][10]. The data provided by these systems enables individuals and decision-makers to make informed decisions during flood events.…”
Section: Introductionmentioning
confidence: 99%
“…Kim et al [11] used three machine learning models, gradient boosting (GB), support vector model (SVM), and long short-term memory (LSTM), for real-time flood forecasting of Namhan river in Korea, by comparing with storage function model (SFM), and the results showed that LSTM model had the best predictive power. Overall, there is a growing interest in real-time prediction of water levels in recent years [12][13][14]. Ren et al [3] investigated short-term water level prediction in the Middle Route of the South-to-North Water Diversion Project using monitoring datasets collected under constant gate opening, which implied no substantial changes in the water level of the open channel (within 0.05 m every 2 h).…”
Section: Introductionmentioning
confidence: 99%