Reliability Study of Shoreline Change Using Monte …dpwe.nctu.edu.tw/uploads/attachment/file/117/Handbo… · Web viewThe analysis and application of artificial neural networks for

International Workshop on Computation, Uncertainty and Risk Assessment in Hydroscience and Engineering

Time Programme Chair08:00-08:30 Registration

08:30-08:40 Opening remark

08:40-09:00 Group photo

09:00-09:30 Uncertainty analysis of flood mapping by using satellite precipitation and hydrologic modelsPao-Shan Yu, NCKU, Taiwan

Keh-Chia Yeh, NCTU, Taiwan09:30-10:00 Mustafa Altinakar, NCCHE, USA

10:00-10:30 Assessing the element of surprise of record-breaking flood eventsThomas Kjeldsen, University of Bath, UK

10:30-10:50 Break

10:50-11:20 Extensive monitoring of sediment transport for reservoir sediment managementChih-Ping Lin, NCTU, Taiwan

Wen-Cheng Liu, TTFRI, Taiwan

11:20-11:50 Development and validation of CCHE2D dam break process model Yafei Jia, NCCHE, USA

11:50-12:20 The analysis and application of artificial neural networks for early warning systems in flood-related applicationsAndrew P Duncan, University of Exeter, UK

12:20-13:30 Lunch

13:30-14:00 Research of radar sciences and engineering at the university of oklahoma – Advanced Radar Research Center (ARRC)Tian-You Yu, University of Oklahoma, USA

Mustafa Altinakar, NCCHE, USA

14:00-14:30 Integrated Coastal Process Modeling and Impact Assessment of Flooding and Sedimentation due to Typhoons in TaiwanYan Ding, NCCHE, USA

14:30-15:00 The application of ensemble rainfall forecasts to social-economic impact assessment during emergency responseJiun-Huei Jang, NCDR, Taiwan

15:00-15:20 Break

15:20-15:50 Towards efficient modelingYaoxin Zhang, NCCHE, USA

Thomas Kjeldsen, University of Bath, UK

15:50-16:20 Linking fluvial and landslide erosions along a meandering river in Southern Taiwan Yi-Chin Chen, NCUE, Taiwan

16:20-16:50 Investigation of the evolution of riverbed and pier scour depths by using water-surface velocity radar and wireless tracersJian-Hao Hong, TTFRI, Taiwan

16:50-17:00 Closing remark


Meeting VenueHao-Ran International Conference Hall, National Chiao Tung University LibraryPhone: 03-5712121 # 52636http://www.lib.nctu.edu.tw/

Local Contact PersonsJosh Yang, Ph.D.Taiwan Typhoon and Flood Research InstituteNational Applied Research LaboratoriesPhone: 02-23219660 #118Email: [email protected]

Chung-Ta Liao, Ph.D.Disaster Prevention & Water Environment Research CenterNational Chiao Tung UniversityPhone: 03-5712121 #55268Email: [email protected]

Pao-Shan Yu, Professor & Dean, Department of Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan

Mustafa Altinakar, Director, National Center for Computational Hydroscience and Engineering, USA


Thomas Kjeldsen, Senior Lecturer, Department of Architecture and Civil Engineering, University of Bath, UK

Chih-Ping Lin, Distinguished Professor, Department of Civil Engineering, National Chiao Tung University, Taiwan

Yafei Jia, Research Professor and Assistant Director, National Center for Computational Hydroscience and Engineering, USA

Andrew P Duncan, Associate Research Fellow, University of Exeter Centre for Water Systems, UK

Tian-You Yu, Professor, School of Electrical and Computer Engineering,Advanced Radar Research Center, and School of Meteorology, University of Oklahoma, USA

Yan Ding, Research Associate Professor, National Center for Computational Hydroscience and Engineering, USA

Jiun-Huei Jang, Assistant Division Head, National Science and Technology for Disaster Reduction, Taiwan

Yaoxin Zhang, Research Scientist, National Center for Computational Hydroscience and Engineering, USA

Yi-Chin Chen, Assistant Professor, Department of Geography, National Changhua University of Education, Taiwan

Jian-Hao Hong, Associate Researcher, Taiwan Typhoon and Flood Research Institute, Taiwan

Speaker List


UNCERTAINTY ANALYSIS OF FLOOD MAPPING BY USING SATELLITE PRECIPITATION AND HYDROLOGIC MODELSPao-Shan Yu1, Soroosh Sorooshian2, Cheng-Shang Lee3, Kuo-Lin Hsu4, Tao-Chang Yang5, Chen-

Min Kuo6, Hung-Wei Tseng7

Ph.D., Dean and Distinguished Professor, Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan. Email: [email protected]., Distinguished Professor, Civil and Environmental Engineering, University of California, Irvine, Irvine, USA. Email: [email protected]., Professor, Atmospheric Science, National Taiwan University, Taiwan. Email: [email protected]., Associate Professor, Civil and Environmental Engineering, University of California, Irvine, Irvine, USA. Email: [email protected]., Associate Research Professor, Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan. Email: [email protected]., Assistant Research Fellow, Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan. Email: [email protected]., Post-Doctoral Fellow, Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan. Email: [email protected]

This study aims at proposing an approach to apply WRF (Weather Research and Forecasting Model) rainfall forecasting, radar rainfall and satellite rainfall to physiographic inundation-drainage model for providing a real-time flood forecasting of Dianbao River in Taiwan. The Dianbao River is a low-relief catchment which is easily affected by the flood disaster. Since the lacks of reliable rainfall forecasting and inundation model, this study tried to derive a selection strategy to refine the rainfall forecasting for better flood simulation.

Various WRF rainfall forecasting results provided by Taiwan Typhoon and Flood Research Institute (TTFRI) are used in this study. WRF can provide 78hr forecasting, but the results among different models are quite different due to their non-isolated boundary condition. Thus, the real-time radar rainfall and satellite rainfall can be used to verify the estimation of WRF. Once the WRF estimations are reliable, the WRF forecasting results can be used to derive the flood inundation depth for the study area. So, the chosen of WRF is the key step for the flood estimation. This study integrated QPESUMS radar rainfall and PERSIANN satellite rainfall to provide better rainfall forecast. The idea is picking up the available WRF rainfall forecasting form PERSIANN or QPESUMS in sea area while the typhoon has been generated. Base on the 6hr-delay rainfall forecasting from 21 sets of WRF model, a pattern recognition method is used to compare the PERSIANN observation to the WRF forecasting for the same time period in every 6hr. With assigning some weighting factors for the 7-12hr WRF rainfall forecasting base on the error between WRF and PERSIANN, we can generate the reliable rainfall forecasting. Also, we may select some reliable rainfall forecasting results for uncertainty analysis. Through the flood inundation map produced by physiographic inundation-drainage model, decision makers can identify flood prone areas and make emergency preparedness.


UNCERTAINTY ANALYSIS OF FLOOD MAPPING BY USING SATELLITE PRECIPITATION AND HYDROLOGIC MODELSPao-Shan Yu1, Soroosh Sorooshian2, Cheng-Shang Lee3, Kuo-Lin Hsu4, Tao-Chang Yang5, Chen-

Min Kuo6, Hung-Wei Tseng7

Ph.D., Dean and Distinguished Professor, Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan. Email: [email protected]., Distinguished Professor, Civil and Environmental Engineering, University of California, Irvine, Irvine, USA. Email: [email protected]., Professor, Atmospheric Science, National Taiwan University, Taiwan. Email: [email protected]., Associate Professor, Civil and Environmental Engineering, University of California, Irvine, Irvine, USA. Email: [email protected]., Associate Research Professor, Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan. Email: [email protected]., Assistant Research Fellow, Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan. Email: [email protected]., Post-Doctoral Fellow, Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan. Email: [email protected]

This study aims at proposing an approach to apply WRF (Weather Research and Forecasting Model) rainfall forecasting, radar rainfall and satellite rainfall to physiographic inundation-drainage model for providing a real-time flood forecasting of Dianbao River in Taiwan. The Dianbao River is a low-relief catchment which is easily affected by the flood disaster. Since the lacks of reliable rainfall forecasting and inundation model, this study tried to derive a selection strategy to refine the rainfall forecasting for better flood simulation.

Various WRF rainfall forecasting results provided by Taiwan Typhoon and Flood Research Institute (TTFRI) are used in this study. WRF can provide 78hr forecasting, but the results among different models are quite different due to their non-isolated boundary condition. Thus, the real-time radar rainfall and satellite rainfall can be used to verify the estimation of WRF. Once the WRF estimations are reliable, the WRF forecasting results can be used to derive the flood inundation depth for the study area. So, the chosen of WRF is the key step for the flood estimation. This study integrated QPESUMS radar rainfall and PERSIANN satellite rainfall to provide better rainfall forecast. The idea is picking up the available WRF rainfall forecasting form PERSIANN or QPESUMS in sea area while the typhoon has been generated. Base on the 6hr-delay rainfall forecasting from 21 sets of WRF model, a pattern recognition method is used to compare the PERSIANN observation to the WRF forecasting for the same time period in every 6hr. With assigning some weighting factors for the 7-12hr WRF rainfall forecasting base on the error between WRF and PERSIANN, we can generate the reliable rainfall forecasting. Also, we may select some reliable rainfall forecasting results for uncertainty analysis. Through the flood inundation map produced by physiographic inundation-drainage model, decision makers can identify flood prone areas and make emergency preparedness.


ASSESSING THE ELEMENT OF SURPRISE OF RECORD-BREAKING FLOOD EVENTS

Thomas Kjeldsen1

Ph.D., Senior Lecturer, Department of Architecture and Civil Engineering, University of Bath, UK. Email: [email protected]

The occurrence of record-breaking flood events continuous to cause damage and disruption despite significant investments in flood defences, suggesting that these events are in some sense surprising. This study develops a new statistical test to help assess if a flood event can be considered surprising or not. The test statistic is derived from annual maximum series (AMS) of extreme events, and Monte Carlo simulations were used to derive critical values for a range of significance levels based on a Generalized Logistic distribution. The method is tested on a national dataset of AMS of peak flow from the United Kingdom, and is found to correctly identify recent large event that have been identified elsewhere as causing a significant change in UK flood management policy. No temporal trend in the frequency or magnitude of surprising events was identified, and no link could be established between the occurrences of surprising events and large-scale drivers.

EXTENSIVE MONITORING OF SEDIMENT TRANSPORT FOR RESERVOIR SEDIMENT MANAGEMENT

Chih-Ping Lin1

1Ph.D., Distinguished Professor, Department of Civil Engineering, National Chiao Tung University, Hsinchu, Taiwan. Email: [email protected]

Sedimentation is a serious threat to long-term water resource management worldwide. In particular, reservoir sedimentation is becoming more serious in Taiwan due to geological weathering and climate change in watersheds. Large amount of sediments transports to reservoirs during storm events at hyperpycnal concentration. Full-event monitoring of sediment transport in a reservoir plays an important role in sustainable reservoir management. This presentation begins by reviewing existing surrogate techniques in need for monitoring suspended-sediment transport with high concentration range and wide spatial coverage. More commercially available techniques suffer from particle size dependency and limited measurement range. A relatively new technique based on time domain reflectometry is introduced. It possesses several advantages, including particle-size independence, high measurement range, durability, and cost-effective multiplexing. Its application in an extensive SSC monitoring program for reservoir management is demonstrated through a case study in Shihmen reservoir, Taiwan. Monitoring stations were installed at the major inflow river mouth and outlet works with fixed protective structures to provide inflow and outflow sediment-discharge records. To capture the characteristics of density currents, a multi-depth monitoring station was designed and deployed on floating platforms in the reservoir. Some of the data collected during Typhoons are presented as an example to demonstrate the effectiveness and benefits of the TDR-based monitoring program.


DEVELOPMENT AND VALIDATION OF CCHE2D DAM BREAK PROCESS MODEL

Yafei Jia1, Yaoxin Zhang2

1Ph.D., Research Professor and Assistant Director, National Center for Computational Hydroscience and Engineering, USA. Email: [email protected] Scientist, National Center for Computational Hydroscience and Engineering, USA.Email: [email protected]

Flooding due to earth embankment breaching often results in detrimental impact on the people and their properties in the flooding zone. The embankment breaching is often caused by overtopping of the excessive water in a reservoir/river; the breaching process is dominated by the shape, soil property of the embankment and the flow conditions of the reservoir/river.

A practical numerical simulation model for overtopping embankment breaking process is developed in this study. Because we would like the model to represent the hydrodynamics and soil erosion processes as much as possible, , the key physical-empirical dam breaking mechanisms of earth embankment are adopted and implemented into a depth integrated free surface flow model CCHE2D (Jia, et al. 2002). A special function representing the shape of the breaching channel profile is introduced which greatly simplifies the effort of modeling. The shear stress of the flow over the cohesive earth of the breaching embankment is simulated by the 2D flow model.

The developed model is validated using experiment data collected by Henson et al. (2005). The simulated flooding hydrograph, headcut migration and breaching embankment profiles agree with the observation very well. For general users’ applications, the graphic user interface of the CCHE2D model is modified to include this capability of the developed model. Because the breaching model is associated to a 2D general flow model, it is possible to simulate multiple embankment breachings in general and complex flow situations; the applicability of the dam-break models are thus broadened significantly.


THE ANALYSIS AND APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR EARLY WARNING SYSTEMS IN FLOOD-RELATED

APPLICATIONSAndrew Paul Duncan1

1Ph.D., Associate Research Fellow, University of Exeter Centre for Water Systems, UK. Email: [email protected]

Artificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including flood prediction and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warning Systems (EWS) might not wish to take the risk of implementing solutions perceived as containing unknown elements, despite the significant computational advantages that ANNs offer. This presentation therefore builds on existing efforts to open the box and develop tools and techniques that visualise, analyse and use ANN weights and biases especially from the viewpoint of neural pathways from inputs to outputs of feedforward networks. In so doing, it aims to demonstrate novel approaches to self-improving predictive model construction for both regression and classification problems. This includes Neural Pathway Strength Feature Selection (NPSFS), which uses ensembles of ANNs trained on differing subsets of data and analysis of the learnt weights to infer degrees of relevance of the input features and so build simplified models with reduced input feature sets and improved predictive performance.

Case studies are carried out for prediction of flooding at multiple nodes in urban drainage networks located in three urban catchments in the UK, which demonstrate rapid, accurate prediction of flooding both for regression (flood depths and volumes) and classification (severity levels). By exploiting the similarities between hydrograph shapes at different nodes in the sewer network we demonstrate that it is possible to build predictive models for multiple sewer nodes using a single multi-output ANN. Predictive skill is shown to reduce beyond the time of concentration of each sewer node, when actual rainfall is used as input to the models – indicating the need for predictions of rainfall to be used to achieve operationally useful prediction times.

Results from ANN model ensembles generally exhibit improved performance, when compared with single ANN models. Also ensembles with reduced input feature sets, using NPSFS, demonstrate as good or improved performance when compared with the full feature set models.

Conclusions are drawn about a new set of ANN-based tools and techniques, including NPSFS and visualisation techniques for inspection of ANN weights, the adoption of which it is hoped may lead to improved confidence in the use of ANN for live real-time flood-related Early Warning System applications.


RESEARCH OF RADAR SCIENCES AND ENGINEERING AT THE UNIVERSITY OF OKLAHOMA – ADVANCED RADAR RESEARCH CENTER

(ARRC)Tian-You Yu1

1Ph.D., Professor, School of Electrical and Computer Engineering, Advanced Radar Research Center, and School of Meteorology, University of Oklahoma, USA. Email: [email protected]

The Advanced Radar Research Center (ARRC) at the University of Oklahoma was established in 2005 by leveraging the legacy of weather radar research, development, application, and operation in Norman achieved through unique synergy among university, government laboratories and private sectors. Since then, the ARRC has grown to be arguably one of the largest academic research centers in the US focused on advancements in weather radar. Currently, ARRC has 18 faculty members, eight engineers in radar software, hardware, and mechanical design and development, five administrative staff and more than 100 undergraduate and graduate students, postdocs, and visiting scholars from meteorology, hydrology, and engineering. The ARRC’s mission is solving challenging radar research problems, preparing students to become the next generation of scientists and engineers, and serving to empower economic growth and development in the field of weather radar. Recently, ARRC has expended its portfolio to include radar research in the areas of Defense, Security and Intelligence. ARRC’s areas of emphasis exist in rapid hardware prototyping, advanced signal processing, antennas, hydrometeorology, clear-air sensing, UAS sensors, severe weather, applied electromagnetics, and microwave engineering. Through the collaborative nature instilled in its members, the ARRC has proven effective at developing synergy between science and engineering in the field of radar. In the National Weather Center and in its extensive laboratory and radar facilities, meteorology, hydrology and engineering faculty and students work side-by-side to learn from each other and to tackle challenging problems in remote sensing, microwave engineering, and applied electromagnetics. In this presentation, the ARRC facilities will be briefly introduced and the ARRC’s areas of expertise will be discussed with the focus on the innovative development and application of radars to mitigate the impact of natural hazards such as severe storms, flash flood, etc.


INTEGRATED COASTAL PROCESS MODELING AND IMPACT ASSESSMENT OF FLOODING AND SEDIMENTATION DUE TO TYPHOONS

IN TAIWANYan Ding1

1Ph.D., Research Associate Professor, Interim Associate Director of Administration, National Center for Computational Hydroscience and Engineering, The University of Mississippi, University, MS 38677, USA. Email: [email protected]

Hazardous storms and typhoons/hurricanes can be devastating by causing flooding water inundations, shoreline erosions, navigation channel refilling, and casualties. For risk analysis and coastal protection planning, it is essential to simulate and predict multiscale physical processes during storms due to severe changes in waves, storm surges, sea levels, river flooding flows, and sediment transport and morphology from rivers, estuaries, to coasts. For planners and decision-makers to assess socio-economic and environmental impacts of extreme tropical storms and climate changes, integrated coastal process modeling has become a major approach to facilitate multiple-purpose engineering practices in developing cost-effective coastal flood management plans, as well as designing erosion control structures.

This presentation gives a brief review on integrated coastal process modeling including an integrated modeling system, CCHE2D-Coast, developed in the National Center for Computational Hydroscience and Engineering. Then it focuses on the application cases of this model for coastal and estuarine planning by assessing the impact of coastal flooding and sedimentation due to typhoons which made landfalls in Taiwan. It demonstrates model validation by simulating hydrodynamic and morphodynamic processes (sediment transport and bed changes) due to recent multiple typhoons in the Tamsui and Touchien Estuaries. Simulation results of the model serve the purpose of assessing coastal flooding risks, navigation channel refilling, and shoreline erosions, and identifying cost-effective engineering protection plans in the estuaries.

THE APPLICATION OF ENSEMBLE RAINFALL FORECASTS TO SOCIAL-ECONOMIC IMPACT ASSESSMENT DURING EMERGENCY RESPONSE

Jiun-Huei Jang1

1Ph.D., Assistant Division Head, National Science and Technology for Disaster Reduction, Taiwan. Email: [email protected]

Typhoon Soudelor brought tremendous rainfall to Taiwan during 8/7—8/8 in Aug., 2005, causing severe river water surge, flash flooding and debris flow to the north area of Taiwan. Taking Soudelor as an example, this study investigates the application of grid-based ensemble rainfall forecast products to evaluate the socio-economic impacts related to meteo-hydrological disasters in real time. Efforts are specially put on the discussion of prediction uncertainty and accuracy from the angle of lead-time in emergency responses.


TOWARDS EFFICIENT MODELINGYaoxin Zhang1, Yafei Jia2

1Research Scientist, National Center for Computational Hydroscience and Engineering, USA.Email: [email protected]., Research Professor and Assistant Director, National Center for Computational Hydroscience and Engineering, USA. Email: [email protected]

CFD (Computational Fluids Dynamics) analyses are playing more and more important roles in water resource related problems. However the analyses are often time-consuming when they are applied to large-scale, long-term, and computation-intensive problems. The situation becomes more challenging in the mega data time, when a huge amount of data requires efficient computation, treatment and interpretation. CCHE modeling system developed at NCCHE (National Center for Computational Hydro-science and Engineering) is a widely used software for water resource problems. Recently, advanced models and techniques have been developed to enhance its efficiency. The following modeling enhancements will be presented in the workshop.

1) CCHE1D channel network model: when seeking the average hydrology/hydraulic solutions, 1D model is the most suitable and efficient tool for large-scale and long-term problems. The newly developed CCHE1D GUI is focused on 64-bit version to satisfy users’ mega data requirements.

2) Sub-domain method: 2D and 3D are generally more accurate modeling methods. To improve their computation efficiency for large domains, a novel sub-domain method has been developed. The method allow us to obtain dense and sophisticated 2D/3D results efficiently at the interested locations in a large domain covered with a coarse mesh.

Parallel computing: as an important technology and standard solution, parallel computing is able to significantly enhance efficiency by distributing computation loads to multiple processers working at the same time. In CCHE modeling system, a 2D parallel computing module for GPGPU (General-purpose computing on Graphics Processing Unit) has been developed for both general flow and dam-break flow/flooding simulations. Applications have demonstrated its high computing efficiency.


LINKING FLUVIAL AND LANDSLIDE EROSIONS ALONG A MEANDERING RIVER IN SOUTHERN TAIWAN

Yi-Chin Chen1, Kang-Tsung Chang2, Jui-Yi Ho3

1Ph.D., Assistant Professor, Department of Geography, National Changhua University of Education, Taiwan. Email: [email protected]., Professor, Department of Geography, National Taiwan University, Taiwan. Email: [email protected]., Assistant Researcher, Hydrotech Division, Taiwan Typhoon and Flood Research Institute, National Applied

Research Laboratories, Taiwan. Email: [email protected]

Fluvial erosion is an important geomorphologic process that induces both vertical incision on the stream bed and lateral erosion on the toe of adjacent hillslopes. After the materials are removed, rock fall and landslide are triggered on hillslopes. Although it has been long expected that fluvial erosion can trigger landslide, relatively few studies have been conducted on quantifying the effects of fluvial erosion on landslide and the application of these factors to landslide prediction. This study linked the effects of fluvial erosion on landslide erosion in the particular meanders landscape in the Jhoukou river watershed, southern Taiwan. A semi-automatic model was developed to extract various fluvial factors, e.g. sinuosity, stream power index, and stream order, and to build the spatial linkage of river to hillslope by using geographic information system techniques. To quantify landslide erosion rate, the area of landslides for 11 events in 2001-2010 were mapped from satellite images or orthophotos, and a volume-area relation was then used to estimate the landslide volume. The results showed that stream sinuosity, slope gradient, and unit stream power were significantly correlated with the landslide erosion rate. The rates on the undercut, slip-off, and head-valley hillslopes were 36.9 mm/yr, 26.5 mm/yr, and 30.4 mm/yr, respectively, and were different significantly among each other. Moreover, landslide erosion rates increased with sinuosity or stream order on the undercut slope, but decreased on the slip-off slope. This suggests that the effects of fluvial erosion play a more important role on the meandering or downstream river than that on the straight or upstream river by eroding the materials on the undercut slopes and depositing sediment on the slip-off slopes. Furthermore, comparing the infinite slope models with and without using fluvial erosion factors, the usage of the fluvial erosion factors can improve the model performance, especially for the downstream area. This study highlights the need to understand more about the fluvial effects on landslide and topography evolution in mountainous areas.


INVESTIGATION OF THE EVOLUTION OF RIVERBED AND PIER SCOUR DEPTHS BY USING WATER-SURFACE VELOCITY RADAR AND

WIRELESS TRACERSJian-Hao Hong1

1Ph.D., Associate Researcher, Hydrotech Division, Taiwan Typhoon and Flood Research Institute, National AppliedResearch Laboratories, Taiwan. Email: [email protected]

Scour around bridge piers and along river reaches has long been an intrigue topic for researchers. Its development, especially during floods or some other large hydrological events, has particularly received plenty of attention. Most research focuses on either building a prediction model or developing a scour monitoring system. However, due to the difficulties in obtaining scour measurements during floods or spontaneous simulations from numerical models, it always poses a great challenge for the administrators or agencies in the right timing for bridge closure or re-opening. This study conducted field measurements of bridge pier and channel bed scour at Mingchu Bridge which crosses the middle section of the Choshui River in Taiwan. Numbed bricks and wireless tracers were used to measure the maximum scour depth and temporal variations of the scour depths during floods. A surface velocity radar and a water-level gauge were also installed on the bridge deck to obtain flow information. Scour data were collected separately during a monsoon and Typhoon Matmo in 2014, with the respective peak flow discharges of 1,446 and 4,980m3/s. The corresponding maximum general and pier scour depths reached 1.76 m and 2.53 m during the monsoon, and 3.245 m and 4.125 m during the typhoon. A quick estimation algorithm for temporal variations of general scour depth was developed, based on the effectively cumulative stream power concept and calibrated by using the field data. Temporal variations of total pier scour depth then could be determined by superimposing the estimation on the local pier scour depth. By examining with the data from these two events, the results showed reasonable agreement with the field measurements. With the quick estimation developed in this study, it would be possible to install guidelines for river and bridge management. More field data are needed to further test the reliability and capability of the proposed method, and a more robust scour monitoring system shall be developed in the future.

Documents

Reliability Study of Shoreline Change Using Monte …dpwe.nctu.edu.tw/uploads/attachment/file/117/Handbo… · Web viewThe analysis and application of artificial neural networks for