Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/nhess-2017-248
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
26 Jul 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).
Kalman-filter based stochastic-multiobjective network optimization and maximal-distance Latin hypercube sampling for uncertain inundation evacuation planning
Tsang-Jung Chang, Yi-Hsuan Shih, and Ming-Che Hu Department of Bioenvironmental Systems Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan
Abstract. The subject of this research is to develop Kalman-filter based stochastic-multiobjective network optimization and maximal-distance Latin hypercube sampling methods regarding uncertain inundation evacuation planning. First, this research proposes a maximal-distance Latin hypercube sampling method to seek maximal space-filling sampling in uncertain flooding factor space. Uncertain inundation factors including upstream inflow, downstream water level, and channel friction resistance uncertainty are considered. Incorporated with the sampling method, HEC-RAS hydraulic model simulates stochastic flooding scenarios. Next, a Kalman-filter based stochastic-multiobjective network optimization model is established for uncertain inundation evacuation. Kalman-filter method iteratively predicts the flooding state of the next stage and updates prediction and decision according to new measurements. Kalman-filter based stochastic-multiobjective programming determines optimal shelter capacity expansion in the here-and-now stage and the best evacuation planning for each scenario in the wait-and-see stage. A case study of stochastic inundation evacuation in Muzha, Taiwan, is conducted. The contribution of this study is to incorporate Kalman-filter based stochastic-multiobjective network programming, HEC-RAS hydraulic simulation model, and maximal-distance Latin hypercube sampling to analyze inundation evacuation planning under uncertainty. The results show tradeoff between shelter expansion and evacuation time; furthermore, decreasing marginal effect of capacity expansion for evacuation time reduction is presented.

Citation: Chang, T.-J., Shih, Y.-H., and Hu, M.-C.: Kalman-filter based stochastic-multiobjective network optimization and maximal-distance Latin hypercube sampling for uncertain inundation evacuation planning, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-248, in review, 2017.
Tsang-Jung Chang et al.
Tsang-Jung Chang et al.
Tsang-Jung Chang et al.

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Short summary
This study analyzes inundation evacuation under uncertainty. In this study, an efficient (Kalman-filter based stochastic-multiobjective network) model is established for iterative prediction, measurement, update, and optimization of stochastic inundation simulation and evacuation. The tradeoff and uncertainty analysis of evacuation planning is conducted and presented on the GIS platform for decision making.
This study analyzes inundation evacuation under uncertainty. In this study, an efficient...
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