Risk assessment of flood disaster and forewarning model at different spatial-temporal scales

Aiming at reducing losses from flood disaster, risk assessment of flood disaster and forewarning model is studied. The model is built upon risk indices in flood disaster system, proceeding from the whole structure and its parts at different spatial-temporal scales. In this study, on the one hand, it mainly establishes the long-term forewarning model for the surface area with three levels of prediction, evaluation, and forewarning. The method of structure-adaptive back-propagation neural network on peak identification is used to simulate indices in prediction sub-model. Set pair analysis is employed to calculate the connection degrees of a single index, comprehensive index, and systematic risk through the multivariate connection number, and the comprehensive assessment is made by assessment matrixes in evaluation sub-model. The comparison judging method is adopted to divide warning degree of flood disaster on risk assessment comprehensive index with forewarning standards in forewarning sub-model and then the long-term local conditions for proposing planning schemes. On the other hand, it mainly sets up the real-time forewarning model for the spot, which introduces the real-time correction technique of Kalman filter based on hydrological model with forewarning index, and then the real-time local conditions for presenting an emergency plan. This study takes Tunxi area, Huangshan City of China, as an example. After risk assessment and forewarning model establishment and application for flood disaster at different spatial-temporal scales between the actual and simulated data from 1989 to 2008, forewarning results show that the development trend for flood disaster risk remains a decline on the whole from 2009 to 2013, despite the rise in 2011. At the macroscopic level, project and non-project measures are advanced, while at the microcosmic level, the time, place, and method are listed. It suggests that the proposed model is feasible with theory and application, thus offering a way for assessing and forewarning flood disaster risk.

[1]  T. Tingsanchali.  Urban flood disaster management , 2012 .

[2]  Zhou Guan-hua.  Risk assessment system for regional flood disaster (I):principle and method , 2008 .

[3]  Dong-Sin Shih,et al.  Improving our understanding of flood forecasting using earlier hydro-meteorological intelligence , 2014 .

[4]  Regional Rainfall Frequency and Ungauged Basin Analysis for Flood Risk Assessment in Haiti , 2014 .

[5]  Oscar Fontenla-Romero,et al.  An online learning algorithm for adaptable topologies of neural networks , 2013, Expert Syst. Appl..

[6]  T. Jiang,et al.  Comparison of uncertainties in projected flood frequency of the Zhujiang River, South China , 2013 .

[7]  Kejun Zhu,et al.  A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction , 2008, Appl. Math. Comput..

[8]  Seung Oh Lee,et al.  Estimation of flood risk index considering the regional flood characteristics: a case of South Korea , 2014, Paddy and Water Environment.

[9]  C. Neuhold,et al.  Assessing flood risk associated with waste disposals: methodology, application and uncertainties , 2011 .

[10]  H. Kunreuther,et al.  Redesigning Flood Insurance , 2011, Science.

[11]  Emmanouil N. Anagnostou,et al.  Flash floods: Observations and analysis of hydro-meteorological controls , 2010 .

[12]  Juliang Jin,et al.  Risk Assessment of Regional Water Resources and Forewarning Model at Different Time Scales , 2013 .

[13]  M. Borga,et al.  Characterisation of selected extreme flash floods in Europe and implications for flood risk management , 2010 .

[14]  Xi Chen,et al.  Kalman Filtering Correction in Real-Time Forecasting with Hydrodynamic Model , 2008 .

[15]  Nien-Sheng Hsu,et al.  Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system. , 2009 .

[16]  Weimin Ju,et al.  On the attribution of the changing hydrological cycle in Poyang Lake Basin, China , 2014 .

[17]  YE Hai-yun.  Discussion of set pair analysis assessment model based on multi-contact number , 2009 .

[18]  Victor Stefanescu,et al.  Decision support system based on the history of flood and flash flood events in Romania , 2013, Natural Hazards.

[19]  Li-Chiu Chang,et al.  Reinforced recurrent neural networks for multi-step-ahead flood forecasts , 2013 .

[20]  Juliang Jin,et al.  Dynamic risk assessment model for flood disaster on a projection pursuit cluster and its application , 2014, Stochastic Environmental Research and Risk Assessment.

[21]  Ben Gouldby,et al.  Multiobjective optimisation for improved management of flood risk , 2014 .

[22]  Juliang Jin,et al.  Forewarning model for water pollution risk based on Bayes theory , 2014, Environmental Science and Pollution Research.

[23]  Jinchao Xu,et al.  Water Resources Risk Assessment Model based on the Subjective and Objective Combination Weighting Methods , 2016, Water Resources Management.

[24]  Liu Ke-lin.  Theoretical analysis frame based on general entropy and intelligence integration methodology for flood disaster risk management , 2009 .

[25]  Fei Mao,et al.  An assessment of multidimensional flood vulnerability at the provincial scale in China based on the DEA method , 2012, Natural Hazards.

[26]  Juliang Jin,et al.  Dynamic risk assessment model for water quality on projection pursuit cluster , 2012 .

[27]  W. Botzen,et al.  Individual preferences for reducing flood risk to near zero through elevation , 2013, Mitigation and Adaptation Strategies for Global Change.