Tsung-Yi Pan | National Taiwan University (original) (raw)

Papers by Tsung-Yi Pan

EGU General Assembly Conference Abstracts, Apr 1, 2013

ABSTRACT Many metropolises located in lowlands suffer pluvial inundation disaster more than pluvi... more ABSTRACT Many metropolises located in lowlands suffer pluvial inundation disaster more than pluvial flood disaster. During the post-inundation period, some water-borne illnesses would be induced from the polluted area. For improving mitigation strategies, population health risk assessment is an important tool of post-inundation disaster management, especially in the countries suffering tropical cyclones and monsoon with high frequency. Locating in the hot zone of typhoon tracks in the Western Pacific, Taiwan suffers three to five typhoons annually. Furthermore, the trend of 24 global circulation models (GCMs) shows that climate change would enhance rainfall in Taiwan. The purpose of this study is to evaluate the impacts of climate change on the population health associated with pluvial disaster. This study applies the concept that risk is composed by hazard and vulnerability to assess the risk of the population health associated with pluvial disaster. Stochastic simulation of bi-variate Gamma distribution is developed to downscale the GCMs' monthly data to extreme rainfall event scale in time domain. According to A1B scenario in short-term period of climate change, two-dimensional overland-flow coupled with drainage systems simulation is performed based on a design extreme rainfall event to calculate the impacts of climate change on pluvial hazard to population health, including flood depth, velocity and the duration of flood recession. The environmental vulnerability for population health is carried out according to the factors of resident and environment. The risk matrix is applied to show the risk by composing the inundation hazards and vulnerabilities associated with population health. The Taipei City, the Capital of Taiwan, is selected as the case study because the highest density of population in Taiwan causes high exposure to the risk of water-borne illnesses. Through assessing the impacts of climate change on the population health associated with pluvial disaster of the Taipei City, the analytical results of pluvial-induced health risk can provide useful information for setting mitigation strategies of post-inundation disaster management. Keywords: climate change, population health, pluvial disaster.

The intensity and spatial variability of storm rainfalls play an essential role in occurrences of... more The intensity and spatial variability of storm rainfalls play an essential role in occurrences of debris flows. Thus, understanding and characterizing the spatial variability of storm rainfalls is a prerequisite for debris flows mitigation. In this study, we investigate the spatial variabilities of rainfalls induced by different storm types using geostatistics. Summer convective storms are found to exhibit higher degree of rainfall spatial variability than typhoons, Mei-Yu and winter frontal systems. The semi-variogram of hourly rainfalls of convective storms was then used to assist in evaluation and augmentation of an existing raingauge network in Taipei.

EGU General Assembly Conference Abstracts, Apr 1, 2016

Water, Nov 30, 2019

Owing to their short duration and high intensity, flash floods are among the most devastating nat... more Owing to their short duration and high intensity, flash floods are among the most devastating natural disasters in metropolises. The existing warning tools-flood potential maps and two-dimensional numerical models-are disadvantaged by time-consuming computation and complex model calibration. This study develops a data-driven, probabilistic rainfall-inundation model for flash-flood warnings. Applying a modified support vector machine (SVM) to limited flood information, the model provides probabilistic outputs, which are superior to the Boolean functions of the traditional rainfall-flood threshold method. The probabilistic SVM-based model is based on a data preprocessing framework that identifies the expected durations of hazardous rainfalls via rainfall pattern analysis, ensuring satisfactory training data, and optimal rainfall thresholds for validating the input/output data. The proposed model was implemented in 12 flash-flooded districts of the Xindian River. It was found that (1) hydrological rainfall pattern analysis improves the hazardous event identification (used for configuring the input layer of the SVM); (2) brief hazardous events are more critical than longer-lasting events; and (3) the SVM model exports the probability of flash flooding 1 to 3 h in advance.

Hydrological Processes, 2005

Many novel techniques for reconstructing rainfall‐runoff processes require hydrometeorologic and ... more Many novel techniques for reconstructing rainfall‐runoff processes require hydrometeorologic and geomorphologic information for modelling. However, certain information is not always measurable. In this paper, we employ a special recurrent neural network to reconstruct the rainfall‐runoff process by using collected rainfall data. In addition, we propose an indirect system identification to overcome the drawback of a traditional, time‐consuming trial‐and‐error search. The indirect system identification is an efficient method to recognize the structure of a recurrent neural network. The unit hydrograph can be derived directly from the weights of the network due to a state‐space form embedded in the recurrent neural network. This improves the link between the weights of the network and the physical concepts that most neural networks fail to connect.The case studies of 41 events from 1966 to 1997 have been implemented in Taiwan's Wu‐Tu watershed, where the runoff path‐lines are short and steep. Two recurrent neural networks and one state‐space model are utilized to simulate the rainfall‐runoff processes for comparison. The results are validated by four criteria: coefficient of efficiency; peak discharge error; time to peak arrival error; total discharge volume error. The resulting data from the recurrent neural network reveal that the neural network proposed herein is appropriate for hydrological systems. Copyright © 2005 John Wiley & Sons, Ltd.

Water Challenge: Balancing the Risks: Hydrology and Water Resources Symposium 2002, 2002

Modeling the rainfall-runoff process is always a difficult task in simulation of the whole hydrol... more Modeling the rainfall-runoff process is always a difficult task in simulation of the whole hydrological cycle. For the purpose of simplifying calculation and enhancing the adaptability, neural networks have been extensively applied to hydrological modeling recently. In this study, a modified state space neural network (MSSNN) was proposed to improve the structures of neural network and to realize the transition of unit hydrograph from the weights of MSSNN. We used a two-step procedure to design the structure of MSSNN and to determine the weights of MSSNN. In simulation of the rainfall-runoff process by MSSNN, we adopted a dynamical gradient descent learning algorithm to update the weights in every time step. As a result, the transition of unit hydrograph was obtained based on the changes of the weights of MSSNN.

International Journal of Environmental Research and Public Health, Jul 24, 2020

Large-scaled disaster events had increasingly occurred worldwide due to global and environmental ... more Large-scaled disaster events had increasingly occurred worldwide due to global and environmental change. Evidently, disaster response cannot rely merely on the public force. In the golden hour of crisis, not only the individuals should learn to react, protect themselves, and try to help each other, but also the local school, enterprise, non-government organization (NGO), nonprofit organization (NPO), and volunteer groups should collaborate to effectively deal with disaster events. New Taipei City (NTPC), Taiwan, was aware of the need for non-public force response and therefore developed the process of enhancing local disaster management networks through promoting the resilient community since 2009. The concept of a resilient community is to build community-based capacity for mitigation, preparedness, response, and recovery in an all-hazards manner. This study organized the NTPC experience and presented the standard operation procedure (SOP) to promote the resilient community, key obstacles, maintenance mechanism, and the successful formulation of the local disaster management network. The performance of the promotion was evaluated through a questionnaire survey and found that participants affirmed the positive effect of building community capacity through the entire process. In general, the resilient community as the center of the local disaster management work is shown promising to holistically bridge the inner/outer resources and systematically respond to disaster events.

Natural Hazards and Earth System Sciences, Mar 11, 2011

This study attempts to achieve real-time rainfallinundation forecasting in lowland regions, based... more This study attempts to achieve real-time rainfallinundation forecasting in lowland regions, based on a synthetic potential inundation database. With the principal component analysis and a feed-forward neural network, a rainfall-inundation hybrid neural network (RiHNN) is proposed to forecast 1-h-ahead inundation depth as hydrographs at specific representative locations using spatial rainfall intensities and accumulations. A systematic procedure is presented to construct the RiHNN, which combines the merits of detailed hydraulic modeling in flood-prone lowlands via a two-dimensional overland-flow model and time-saving calculation in a real-time rainfall-inundation forecasting via ANN model. Analytical results from the RiHNNs with various principal components indicate that the RiHNNs with fewer weights can have about the same performance as a feed-forward neural network. The RiHNNs evaluated through four types of real/synthetic rainfall events also show to fit inundation-depth hydrographs well with high rainfall. Moreover, the results of real-time rainfall-inundation forecasting help the emergency manager set operational responses, which are beneficial for flood warning preparations.

Journal of Hydrology, Sep 1, 2004

Rainfall-runoff processes are dynamic systems that are better described by a dynamic model. In th... more Rainfall-runoff processes are dynamic systems that are better described by a dynamic model. In this paper, a specific dynamic neural network, called state space neural network (SSNN), is modified to perform short term rainfall-runoff forecasts. The lead time is extended to 3 h. To improve the link between the weights of the network and physical concepts that most neural networks lack for, a method of the unit hydrograph representation is proposed to reproduce the unit hydrographs based on the weights of the network. Hence, a transition of rainfall-runoff systems can be observed via the changes of the unit hydrograph hour by hour. Furthermore, a new learning method developed from the interchange of the roles of the network states and the weight matrix is applied to train the SSNN and helps the network to evolve into a time-variant model while forecasting the rainfall-runoff process. A study case has been implemented in Taiwan's Wu-Tu watershed, where the runoff path-lines are short and steep. Forty-seven events from 1966 to 1997 are forecasted via the SSNN, and the results are validated via four criteria. The convergence of the new learning algorithm is shown during the model training process. Performance of the SSNN for short term rainfall-runoff forecasting reveals that the specific dynamic recurrent neural network is appropriate for hydrological forecasts.

Water, Feb 18, 2019

Pluvial floods are the most frequent natural hazard impacting urban cities because of extreme rai... more Pluvial floods are the most frequent natural hazard impacting urban cities because of extreme rainfall intensity within short duration. Owing to the complex interaction between rainfall, drainage systems and overland flow, pluvial flood warning poses a challenge for many metropolises. Although physical-based flood inundation models could identify inundated locations, hydrodynamic modeling is limited in terms of computational costs and sophisticated calibration. Thus, herein, a quick pluvial flood warning system using rainfall thresholds for central Taipei is developed. A tabu search algorithm is implemented with hydrological-analysis-based initial boundary conditions to optimize rainfall thresholds. Furthermore, a cross test is adopted to evaluate the effect of each rainfall event on rainfall threshold optimization. Urban sewer flood is simulated via hydrodynamic modeling with calibration using crowdsourced data. The locations and time of occurrence of pluvial floods can be obtained to increase the quality of observed data that dominate the accuracy of pluvial flood warning when using rainfall thresholds. The optimization process is a tabu search based on flood reports and observed data for six flood-prone districts in central Taipei. The results show that optimum rainfall thresholds can be efficiently determined through tabu search and the accuracy of the issued flood warnings can be significantly improved.

Dìqiú kēxué jíkān, 2011

Typhoon Morakot 2009, with significant southwest monsoon flow, produced a record-breaking rainfal... more Typhoon Morakot 2009, with significant southwest monsoon flow, produced a record-breaking rainfall of 2361 mm in 48 hours. This study hopes to improve a statistical typhoon rainfall forecasting method used over the mountain region of Taiwan via an artificial neural network based southwest monsoon enhancement (ANNSME) model. Rainfall data collected at two mountain weather stations, ALiShan and YuShan, are analyzed to establish the relation to the southwest monsoon moisture flux which is calculated at a designated sea area southwest of Taiwan. The results show that the moisture flux, with southwest monsoon flow, transported water vapor during the landfall periods of Typhoons Mindulle, Bilis, Fungwong, Kalmaegi, Haitaing and Morakot. Based on the moisture flux, a linear regression is used to identify an effective value of moisture flux as the threshold flux which can enhance mountain rainfall in southwestern Taiwan. In particular, a feedforward neural network (FNN) is applied to estimate the residuals from the linear model to the differences between simulated rainfalls by a typhoon rainfall climatology model (TRCM) and observations. Consequently, the ANNSME model integrates the effective moisture flux, linear rainfall model and the FNN for residuals. Even with very limited training cases, our results indicate that the ANNSME model is robust and suitable for improvement of TRCM rainfall prediction. The improved prediction of the total rainfall and of the multiple rainfall peaks is important for emergency operation.

Advances in Water Resources, Aug 1, 2007

Characterizing the dynamic relationship between rainfall and runoff is a highly interesting model... more Characterizing the dynamic relationship between rainfall and runoff is a highly interesting modeling problem in hydrology. This study develops a deterministic linearized recurrent neural network (denoted as DLRNN) that deals with the system's nonlinearity by recalibration at each time interval, and relates the weights of DLRNN to unit hydrographs in order to describe the transition of the rainfall-runoff processes. Case studies of 38 events, from 1966 to 1997, are implemented in the Wu-Tu watershed of Taiwan, where the runoff path-lines are short and steep. A comparison between the DLRNN and a feed-forward neural network demonstrates the advantage of DLRNN as a dynamic system model. It is concluded that DLRNN shows superiority in the performance of rainfall-runoff simulations and the ability to recognize transitions in hydrological processes.

Natural Hazards, Jan 5, 2012

Taiwan suffers from an average of three or four typhoons annually, and the inundation caused by t... more Taiwan suffers from an average of three or four typhoons annually, and the inundation caused by the heavy precipitation that is associated with typhoons frequently occurs in lowlands and floodplains. Potential inundation maps have been widely used as references to set up non-structural strategies for mitigating flood hazards. However, spatiotemporal rainfall distributions must be addressed to improve the accuracy of inundation forecasting for emergency response operations. This study presents a system for 24-hahead early warning of inundation, by coupling the forecasting of typhoon rainfall with the modeling of overland flow. A typhoon rainfall climatology model (TRCM) is introduced to forecast dynamically the spatiotemporal rainfall distribution based on typhoon tracks. The systematic scheme for early warning of inundation based on the spatiotemporal downscaling of rainfall and 2D overland-flow modeling yields not only the extent of inundation, but also the time to maximum inundation depth. The scheme is superior to traditional early warning method referring to the maximum extent and depth of inundation determined from conditional uniform rainfall. Analytical results show that coupling TRCM with an overland-flow model yields satisfactory inundation hydrographs for warning of the extent and peak time of inundation. This study also shows that the accuracy of forecasting

Hydrological Sciences Journal-journal Des Sciences Hydrologiques, Jul 3, 2014

Abstract The generation of reliable quantitative precipitation estimations (QPEs) through use of ... more Abstract The generation of reliable quantitative precipitation estimations (QPEs) through use of raingauge and radar data is an important issue. This study investigates the impacts of radar QPEs with different densities of raingauge networks on rainfall–runoff processes through a semi-distributed parallel-type linear reservoir rainfall–runoff model. The spatial variation structures of the radar QPE, raingauge QPE and radar-gauge residuals are examined to review the current raingauge network, and a compact raingauge network is identified via the kriging method. An analysis of the large-scale spatial characteristics for use with a hydrological model is applied to investigate the impacts of a raingauge network coupled with radar QPEs on the modelled rainfall–runoff processes. Since the precision in locating the storm centre generally represents how well the large-scale variability is reproduced; the results show not only the contribution of kriging to identify a compact network coupled with radar QPE, but also that spatial characteristics of rainfalls do affect the hydrographs. Editor Z.W. Kundzewicz; Guest editor R.J. Moore Citation Pan, T.-Y., Li, M.-Y., Lin, Y.-J., Chang, T.-J., Lai, J.-S., and Tan, Y.-C., 2014. Sensitivity analysis of the hydrological response of the Gaping River basin to radar-raingauge quantitative precipitation estimates. Hydrological Sciences Journal, 59 (7), 1335–1352. http://dx.doi.org/10.1080/02626667.2014.923969

Hydrological Sciences Journal-journal Des Sciences Hydrologiques, Oct 21, 2013

Abstract Runoff discharge in the Tuku lowlands, Taiwan, has increased with land development. Freq... more Abstract Runoff discharge in the Tuku lowlands, Taiwan, has increased with land development. Frequent floods caused by extreme weather conditions have resulted in considerable economic and social losses in recent years. Currently, numerous infrastructures have been built in the lowland areas that are prone to inundation; the measures and solutions for flood mitigation focus mainly on engineering aspects. Public participation in the development of principles for future flood management has helped both stakeholders and engineers. An integrated drainage–inundation model, combining a drainage flow model with a two-dimensional overland-flow inundation model is used to evaluate the flood management approaches with damage loss estimation. The proposed approaches include increasing drainage capacity, using fishponds as retention ponds, constructing pumping stations, and building flood diversion culverts. To assess the effects on the drainage system of projected increase of rainfall due to climate change, for each approach simulations were performed to obtain potential inundation extent and depth in terms of damage losses. The results demonstrate the importance of assessing the impacts of climate change for implementing appropriate flood management approaches. Editor Z.W. Kundzewicz Citation Chang, H.-K., Tan, Y.-C., Lai, J.-S., Pan, T.-Y., Liu, T.-M., and Tung, C.-P., 2013. Improvement of a drainage system for flood management with assessment of the potential effects of climate change. Hydrological Sciences Journal, 58 (8), 1581–1597.

Journal of Hydrology, Dec 1, 2013

This paper improves the typhoon flood forecasting over a watershed in a mountainous island of Tai... more This paper improves the typhoon flood forecasting over a watershed in a mountainous island of Taiwan. In the presence of the stiff topography in Taiwan, the typhoon rainfall is often phased-locked with terrain and the typhoon rainfall in general is best predicted by the typhoon rainfall climate model (TRCM) (Lee et al., 2006). However, the TRCM often underestimates the rainfall amount in cases of slowing moving storms with strong southwest monsoon supply of water vapor flux. We apply an artificial neural network (ANN) based southwest monsoon rainfall enhancement (AME) to improve TRCM rainfall forecasting for the Tsengwen Reservoir watershed in the southwestern Taiwan where maximum typhoon rainfall frequently occurred. Six typhoon cases with significant southwest monsoon water vapor flux are used for the test cases. The precipitations of seven rain gauge stations in the watershed and the southwest monsoon water vapor flux are analyzed to get the spatial distribution of the effective water vapor flux threshold, and the threshold is further used to build the AME model. The results indicate that the flux threshold is related to the topographic lifting of the moist air, with lower threshold in the upstream high altitude stations in the watershed. The lower flux threshold allows a larger rainfall amount with AME. We also incorporated the rainfall prediction with a state space neural network (SSNN) to simulate rainfall-runoff processes. Our improved method is robust and produces better flood predictions of total rainfall and multiple rainfall peaks. The runoff processes in the watershed are improved in terms of coefficient of efficiency, peak discharge, and total volume.

In recent years, the impact of climate change and extreme weather has not only expanded the scale... more In recent years, the impact of climate change and extreme weather has not only expanded the scale of disasters, but also increased the frequency of disasters. In order to reduce the impact of natural disasters on cities, Making Cities Resilient (MCR) 2030 promoted by the international community has become an important issue. This study uses the "Ten Essentials" Toolkit for Resistant City constructed by the UNDRR to evaluate the disaster prevention and resilience capabilities of cities. However, the key to quantifying urban resilience is to link the indicators of the Disaster Resilience Scorecard with the operations of local government departments to strengthen urban resilience. Taking Taipei City as an example, this study uses the causal loop diagram (CLD) method to explore the business relationship between "Ten Essentials" and various bureaus, and builds a localized disaster resistance scorecard through expert meetings. CLD provides a visual map of the links bet...

本研究之目的係探討狀態空間降雨–逕流模式之系統識別，並結合線性動態理論與智慧型控制理論發展狀態空間類神經網路降雨–逕流預報模式。最後研析合適之狀態空間類神經網路生成法，並應用於流域之洪水預報。 ... more 本研究之目的係探討狀態空間降雨–逕流模式之系統識別，並結合線性動態理論與智慧型控制理論發展狀態空間類神經網路降雨–逕流預報模式。最後研析合適之狀態空間類神經網路生成法，並應用於流域之洪水預報。模擬水文循環系統中之降雨–逕流歷程為一相當困難之工作。為考量精簡演算過程及增進模式之適用性，本研究應用動態系統理論以研析水文模式之轉換系統，並採用間接系統檢定方法，對水文歷程作深入之探討。文中進一步結合類神經網路發展出具狀態空間特性之狀態空間類神經網路模式，採用整合多種遞迴式類神經網路演算法後所得之統合演算法進行模式參數訓練學習之工作，以期即時更新、校正模式，並對模式參數之變化作深入之探討。一般水文模擬結果之好壞端賴模式之架構及參數之正確性，因此狀態空間類神經網路之生成法有其研究之重要性。本研究研析間接系統檢定法與子空間檢定法之優劣，並深入探討架構模式之過程，期冀能提高模擬降雨–逕流歷程之精確度。研究中選取基隆河中上游五堵集水區民國55年至86年間颱洪事件之記錄降雨與逕流資料，分析定率性降雨–逕流模式之機制。間接系統檢定法乃依據最佳化理論求得系統之單位歷線，進一步估算狀態空間方程式與觀測方程式之參數矩陣，以確知系統之轉換過程。最後，狀態空間類神經網路生成法之研析過程中，考慮間接系統檢定法與直接子空間檢定法進行系統識別。兩種系統檢定法皆採用奇異值分解之數值運算。而藉由有系統之測試瞭解間接系統檢定法與直接子空間檢定法之優缺點後，本研究提出結合兩種檢定法優點之狀態空間類神經網路生成法。研究中選取部分歷年來颱洪事件之記錄降雨與逕流資料，訓練檢定生成之狀態空間類神經網路並進行模式之驗證。本研究所採用之狀態空間類神經網路生成法及獲致之成果，期冀可提供臺灣集水區防洪規劃及水土保持研析之參考應用。The purposes of this study are to discuss the system identification of a state space rainfall-runoff model, and to integrate linear dynamic theory with intelligent control theory to develop a state space neural network rainfall-runoff forecasting m...

International Journal of Disaster Risk Reduction