Browsing by Subject "Storm surge"
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Item Data-driven modeling for compound flooding simulation(2021-08-05) Li, Wei (Ph. D. in computational science, engineering, and mathematics); Dawson, Clint N.; Hodges, Ben; Bui-Thanh, Tan; Ghattas, Omar; Gamba, IreneAccurate simulation of compound flooding is crucial for flood risk management. In coastal regions, both rainfall runoff and storm surge can contribute to flooding and are not mutually exclusive. It is thus necessary to develop a modeling framework for compound flooding that considers both mechanisms. While many hydrologic models have been developed to simulate the rainfall runoff processes, a lot of these methods are either computationally expensive, or incapable of simulating extreme weather events. Thus, they may not be suitable for coupled modeling of compound flooding. In this study, a data-driven hydrologic model based on deep recurrent neural network (RNN) is developed for rainfall runoff simulation at relatively low computational cost. To test the capability of the method, the model is used to infer the streamflow out of an urban watershed, Brays Bayou in Houston, Texas. And the model is validated with real world hydrologic data. Additionally, the proposed synced sequence to sequence RNN architecture is compared with the sequence input single output one that is widely-used in hydrologic modeling. Numerical experiments show that the proposed method provides more accurate predictions using relatively less computational resources than the sequence input single output architecture. Later, downstream water level input is integrated into the RNN model to enable the one-way coupling of rainfall runoff with storm surge. Numerical examples at two different locations demonstrate that the additional information leads to improved predictions. Finally, a two-way dynamic coupling framework is constructed for the RNN hydrologic model and an ocean circulation model, ADvanced CIRCulation. The framework is tested, verified, and validated for the Houston ship channel - Galveston bay estuarine system during Hurricane Harvey (2017). This dissertation is based on the following articles: High temporal resolution rainfall runoff modelling using Long-Short-Term-Memory (LSTM) networks by Wei Li, Amin Kiaghadi, Clint Dawson [1]; Exploring the best sequence LSTM model- ing architecture for flood prediction by Wei Li, Amin Kiaghadi, Clint Dawson [2]; and Simulating compound floods: dynamic coupling of deep learning and physics- based models by Wei Li, Gajanan Choudhary, Amin Kiaghadi, Clint Dawson. This material is based upon work funded by National Oceanic and Atmospheric Admin- istration (Grant No. NA18NOS0120158) and National Science Foundation (NSF, CMMI-1520817).Item Model for estimating damages on power systems due to hurricanes(2010-05) Krishnamurthy, Vaidyanathan; Kwasinski, Alexis; Baldick, RossHurricanes are a threat to power and telecommunication infrastructure. This work summarizes a method for hurricane characterization using the proposed Localized Tropical Cyclone Intensity Index(LTCII) as a model for estimating damages to Electric power infrastructure. The model considers the effect of storm surge, maximum sustained wind speeds, the duration of time for which the system has been under tropical storm conditions and the area swept by hurricane over land. The measurements focus on major load centers in the system. The validation of the outage data is discussed. The model is evaluated for hurricanes from 2004, 2005 and 2008 hurricane seasons. The degree of influence of various hurricane parameters on the damages suffered by electric power systems are discussed using case studies. The maximum outages are observed to follow a logistic regression curve with respect to log(LTCII), with a correlation of 0.85. The observed restoration times fit a 6th degree polynomial with an R2 = 0.6. The effects of time under tropical storm winds were observed to have great significance in the damage profile observed with the model.