Browsing by Author "Yang, Tsun-Hua"

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A General Overview of the Risk-Reduction Strategies for Floods and Droughts
(2020-04-01)
Yang, Tsun-Hua
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Liu, Wen-Cheng
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土木工程學系
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Department of Civil Engineering
Water is a limited resource but essential to sustaining life and supporting economic development. Only 2.5% of all the water on Earth is freshwater and can be used to meet basic human needs such as a clean supply of water for drinking, cooking, and bathing. Water scarcity is the result of an imbalance between supply and demand. Efficient water resource management is definitely of interest to research and is a practical topic. At the same time, water-related disasters such as floods and droughts cause the loss of life and property. Disasters increase the difficulty of effective water resource management. An increase in climate extremes can also increase the risk of floods and droughts. This overview covers 150 peer-reviewed journal publications from the last twenty years focusing on risk-reduction strategies for floods and droughts. First, a definition and classification of flood and drought was introduced. Second, studies and techniques associated with risk reduction were grouped into three themes and discussed: prediction and warning; monitoring; and impact assessment, response, and management. As a result, many studies were solely focused on, and achieve excellence in, their own themes. Special attention was needed to find in these studies what can convert the adverse impacts such as flood water to positive outcomes such as drought relief. Multidisciplinary cooperation is necessary to achieve sustainability and to adapt to climate change. Finally, advanced techniques such as artificial intelligence (AI) and the internet of things (IoT) were foreseen to have a tremendous impact on future disaster risk reduction.
Development of a Hydrological Ensemble Prediction System to Assist with Decision-Making for Floods during Typhoons
(2020-05-01)
Yang, Sheng-Chi
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Yang, Tsun-Hua
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Chang, Ya-Chi
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Chen, Cheng-Hsin
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Lin, Mei-Ying
Hydrological ensemble prediction systems (HEPSs) can provide decision makers with early warning information, such as peak stage and peak time, with enough lead time to take the necessary measures to mitigate disasters. This study develops a HEPS that integrates meteorological, hydrological, storm surge, and global tidal models. It is established to understand information about the uncertainty of numerical weather predictions and then to provide probabilistic flood forecasts instead of commonly adopted deterministic forecasts. The accuracy of flood forecasting is increased. However, the spatiotemporal uncertainty associated with these numerical models in the HEPS and the difficulty in interpreting the model results hinder effective decision-making during emergency response situations. As a result, the efficiency of decision-making is not always increased. Thus, this study also presents a visualization method to interpret the ensemble results to enhance the understanding of probabilistic runoff forecasts for operational purposes. A small watershed with area of 100 km(2) and four historical typhoon events were selected to evaluate the performance of the method. The results showed that the proposed HEPS along with the visualization approach improved the intelligibility of forecasts of the peak stages and peak times compared to that of approaches previously described in the literature. The capture rate is greater than 50%, which is considered practical for decision makers. The proposed HEPS with the visualization method has potential for both decreasing the uncertainty of numerical rainfall forecasts and improving the efficiency of decision-making for flood forecasts.
ECOMSNet - An edge computing-based sensory network for real-time water level prediction and correction
(2020-09-01)
Yang, Tsun-Hua
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Wang, Chia-Wei
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Lin, Sheng-Jhe
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土木工程學系
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Department of Civil Engineering
Water level monitoring and forecasting are essential tasks in flood emergency response. This study proposes an Edge COMputing-based Sensory NETwork (ECOMSNet), an innovative decentralized early warning system (EWS), for water level monitoring and prediction. A sensor-embedded algorithm integrates the direct step method (DSM) with a microgenetic algorithm (MGA). This algorithm predicts the water surface profile and corrects it once water level observations are available. It also meets efficiency requirements to accommodate sensor computation limitations. The errors in the predicted water surface profiles in channels with gradually varied flows are 5% in a laboratory flume experiment and below 10% in a field experiment. The ECOMSNet is an achievement of edge computing-based Internet of Things. It shows potential to increase emergency response efficiency. However, the system requires further refinement and testing if it is to adequately address rapidly varied unsteady flow in a scaled-up implementation.
Typhoon Rainfall Forecasting by Means of Ensemble Numerical Weather Predictions with a GA-Based Integration Strategy
(2018-11-01)
Wu, Ming-Chang
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Yang, Sheng-Chi
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Yang, Tsun-Hua
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Kao, Hong-Ming
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土木工程學系
Rainfall during typhoons is one of the most important water resources in Taiwan, but heavy typhoon rainfall often leads to serious disasters and consequently results in loss of lives and property. Hence, accurate forecasts of typhoon rainfall are always required as important information for water resources management and rainfall-induced disaster warning system. In this study, a methodology is proposed for providing quantitative forecasts of 24 h cumulative rainfall during typhoons. Firstly, ensemble forecasts of typhoon rainfall are obtained from an ensemble numerical weather prediction (NWP) system. Then, an evolutionary algorithm, i.e., genetic algorithm (GA), is adopted to real-time decide the weights for optimally combining these ensemble forecasts. That is, the novelty of this proposed methodology is the effective integration of the NWP-based ensemble forecasts through an evolutionary algorithm-based strategy. An actual application is conducted to verify the forecasts resulting from the proposed methodology, namely NWP-based ensemble forecasts with a GA-based integration strategy. The results confirm that the forecasts from the proposed methodology are in good agreement with observations. Besides, the results from the GA-based strategy are more accurate as compared to those by simply averaging all ensemble forecasts. On average, the root mean square error decreases about 7%. In conclusion, more accurate typhoon rainfall forecasts are obtained by the proposed methodology, and they are expected to be useful for disaster warning system and water resources management during typhoons.
Using numerical weather model outputs to forecast wind gusts during typhoons
(2019-05-01)
Yang, Tsun-Hua
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Tsai, Chin-Cheng
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土木工程學系
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Department of Civil Engineering
Numerical weather prediction (NWP) models are commonly used to predict mean wind speeds. However, methods for predicting gust wind speeds in NWP models are still lacking due to limitations of computer resources. This study develops two models to provide gust wind speed forecasts during a typhoon event (1) a linear regression-based model and (2) a micro-genetic algorithm (MGA)-based model. This study also proposes a successively accumulated regression process that can adaptively update the parameters in the models and thereby improve the forecasts. The performance was evaluated by comparing observations from gauge stations in Taiwan during six typhoons that occurred between 2015 and 2016. The results of the models were compared to a statistical model from a previous study. The results showed that the successively accumulated regression process improves the mean error of the linear regression-based model to 5.23 m/s, compared with 7.08 m/s from the previous study. Meanwhile, the MGA-based model provides the best performance, with a 6.26 m/s time-averaged root mean squared errors for lead times of 7-54 h. This result is superior to the 9.37 m/s obtained with the statistics-based model and the 8.22 m/s obtained with the linear regression-based model.