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Full-Text Articles in Physical Sciences and Mathematics
Evaluating Essential Processes And Forecast Requirements For Meteotsunami-Induced Coastal Flooding, Chenfu Huang, Eric Anderson, Yi Liu, Gangfeng Ma, Greg Mann, Pengfei Xue
Evaluating Essential Processes And Forecast Requirements For Meteotsunami-Induced Coastal Flooding, Chenfu Huang, Eric Anderson, Yi Liu, Gangfeng Ma, Greg Mann, Pengfei Xue
Civil & Environmental Engineering Faculty Publications
Meteotsunamis pose a unique threat to coastal communities and often lead to damage of coastal infrastructure, deluge of nearby property, and loss of life and injury. The Great Lakes are a known hot-spot of meteotsunami activity and serve as an important region for investigation of essential hydrodynamic processes and model forecast requirements in meteotsunami-induced coastal flooding. For this work, we developed an advanced hydrodynamic model and evaluate key model attributes and dynamic processes, including: (1) coastal model grid resolution and wetting and drying process in low-lying zones, (2) coastal infrastructure, including breakwaters and associated submerging and overtopping processes, (3) annual/seasonal …
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Civil & Environmental Engineering Faculty Publications
The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, …
Dynamic Modeling Of Inland Flooding And Storm Surge On Coastal Cities Under Climate Change Scenarios: Transportation Infrastructure Impacts In Norfolk, Virginia Usa As A Case Study, Yawen Shen, Navid Tahvildari, Mohamed M. Morsy, Chris Huxley, T. Donna Chen, Jonathan Lee Goodall
Dynamic Modeling Of Inland Flooding And Storm Surge On Coastal Cities Under Climate Change Scenarios: Transportation Infrastructure Impacts In Norfolk, Virginia Usa As A Case Study, Yawen Shen, Navid Tahvildari, Mohamed M. Morsy, Chris Huxley, T. Donna Chen, Jonathan Lee Goodall
Civil & Environmental Engineering Faculty Publications
Low-lying coastal cities across the world are vulnerable to the combined impact of rainfall and storm tide. However, existing approaches lack the ability to model the combined effect of these flood mechanisms, especially under climate change and sea level rise (SLR). Thus, to increase flood resilience of coastal cities, modeling techniques to improve the understanding and prediction of the combined effect of these flood hazards are critical. To address this need, this study presents a modeling system for assessing the combined flood impact on coastal cities under selected future climate scenarios that leverages ocean modeling with land surface modeling capable …