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Boni Su, Hong Huang, Zhiqiang Wang, Nan Zhang, Wei Zhu, & Xinfeng Wei. (2016). Urban pluvial flood risk assessment based on scenario simulation. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: In this study, urban pluvial flood risk is studied in an actual study area using scenario simulation method based on hydrodynamics. Real weather data and GIS (Geographic Information System) data are adopted to make the results reliable. A region in Haidian District of Beijing is selected as the study area. All the rainfall scenarios (about 200 scenarios) during an 8-year period (from January 1, 2008 to December 31, 2015) are obtained from hourly precipitation data. These rainfall scenarios are used as input for numerical simulations. Spatial-temporal distributions of water depth are obtained through numerical simulation base on SWEs (Shallow-Water Equations). GPU computing technique is applied to increase simulation speed greatly. Influence of rainfall parameters on flood water depth is analyzed. The results show that water depth becomes higher if rainfall duration and average rainfall intensity increase. Moreover, situation of water depth is not only related to overall parameters like rainfall duration or rainfall intensity, but also related to other details of rainfall. Water depth exceedance probability curves of every location and every building are obtained, and different characteristics of the curves are discussed. Finally, the effect of water depth exceedance probability curves of buildings on designing building foundation height is shown. This study is helpful to the risk assessments of urban pluvial flood.
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Xiaoyong Ni, Hong Huang, Shiwei Zhou, Boni Su, Jianchun Zheng, Wei Zhu, et al. (2018). Simulation of The Urban Waterlogging and Emergency Response Strategy at Subway Station's Entry-exit Platform in Heavy Rainstorm. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 99–120). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Underground space like subway stations is prone to be flooded which can lead to severe and unpredictable damage and even threaten human lives. In this paper, four groups of contrastive simulation of urban waterlogging at two subway stations' entry-exit platforms in heavy rainstorm are conducted, and emergency response strategies are suggested. A waterlogging simulation method named UPFLOOD based on shallow water equations is proposed considering complex topography. It has been found that the waterlogging at subway station's entry-exit platforms is easily influenced by several factors and the site selection of the subway stations is very important. A disaster process construction method based on PN model is proposed and it has been found that the response strategies including plugging, drainage and evacuation are important for disaster mitigation. This study helps decision makers to response quickly to meet the emergency of the waterlogging disaster at subway stations caused by heavy rainstorm.
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Yan Wang, Hong Huang, & Wei Zhu. (2015). Stochastic source term estimation of HAZMAT releases: algorithms and uncertainty. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: Source term estimation (STE) of hazardous material (HAZMAT) releases is critical for emergency response. Such problem is usually solved with the aid of atmospheric dispersion modelling and inversion algorithms accompanied with a variety of uncertainty, including uncertainty in atmospheric dispersion models, uncertainty in meteorological data, uncertainty in measurement process and uncertainty in inversion algorithms. Bayesian inference methods provide a unified framework for solving STE problem and quantifying the uncertainty at the same time. In this paper, three stochastic methods for STE, namely Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC) and ensemble Kalman filter (EnKF), are compared in accuracy, time consumption as well as the quantification of uncertainty, based on which a kind of flip ambiguity phenomenon caused by various uncertainty in STE problems is pointed out. The advantage of non-Gaussian estimation methods like SMC is emphasized.
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