Abstract
Aiming at reducing the losses from flood disaster, a dynamic risk assessment model for flood disaster is studied in this article. This model is built upon the projection pursuit cluster principle and risk indexes in the system, proceeding from the whole structure to its component parts. In this study, a fuzzy analytic hierarchy approach is employed to screen out the index system and determine the index weight, while the future value of each index is simulated by an improved back-propagation neural network algorithm. The proposed model adopts a dynamic evaluation method to analyze temporal data and assesses risk development by comprehensive analysis. The projection pursuit theory is used for clustering spatial data. The optimal projection vector is applied to calculate the risk cluster type. Therefore, the flood disaster risk level is confirmed and then the local conditions for presenting the control strategy. This study takes the Tunxi area, Huangshan city, as an example. After dynamic risk assessment model establishment, verification and application for flood disasters between the actual and simulated data from 2001 to 2013, the comprehensive risk assessment results show that the development trend for flood disaster risk is still in a decline on the whole, despite the rise in a few years. This is in accordance with the actual conditions. The proposed model is shown to be feasible for theory and application, providing a new way to assess flood disaster risk.
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Acknowledgments
The authors appreciate the support of the National Natural Science Funds of China (No. 71273081, No. 51309004, No. 51309072, No. 91337108, and No. 41375099), and Nanjing University of Information Science & Technology Research Foundation (S8112077001). The authors also want to thank the anonymous referees for their helpful suggestions and corrections on the earlier draft of our paper according to which we improved the content.
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Zhao, J., Jin, J., Guo, Q. et al. Dynamic risk assessment model for flood disaster on a projection pursuit cluster and its application. Stoch Environ Res Risk Assess 28, 2175–2183 (2014). https://doi.org/10.1007/s00477-014-0881-8
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DOI: https://doi.org/10.1007/s00477-014-0881-8