Grey System Theory And Cloud Model Based Anti-accuracy Analysis Of Flood Disaster

With the rapid development of China's economy and the accelerating process of urbanization, the flood disaster has constrained the economic and social sustainable development, threatened the life and property security, and impeded the process of harmonious social development. According to the uncertainty, the hi-dimensional, complex, open and dynamic characteristics of the flood disaster system, from a system science point of view, flood disaster system is a dynamic complex system. There is no doubt that for the control and management of this complex system, whether classical control theory, or traditional operations research techniques, will encounter difficulties. In this article, based on the comprehensive integration of qualitative and quantitative methods, the system science theories and the anti-accuracy technique based on gray theory and cloud model etc. are introduced in the uncertainty study of flood disaster system. In addition, the integrated analysis approach for the simulation, prediction and evaluation of flood disaster has been systematically explored, for the establishment of uncertainty system theory, anti-accuracy method and technology system for flood disaster analysis. This provides the scientific basis for effective control and management of flood disaster in order to harmoniously develop social economics.Specifically, the article based on the anti-accuracy method of the gray theory and cloud model for analysis of the uncertainty of the flood disaster systems, including disaster prediction, mid- and long-term runoff forecasting, reasoning of life loss, and comprehensive assessment of disaster level. The main research contents and results are as follows:(1) Sorted out the complexity of the flood disaster system and the basic research objective, and analyzed the uncertainty principle and the widespread nature in the real world. On this basis, the anti-accuracy calculation method for analysis of the uncertainty of flood disasters was proposed.(2) Established the gray dynamic models to predict flood disasters on the basis of annual runoff. Taking the Yamadu station in Xinjiang as the research area, the flood disaster was predicted.(3) Based on the gray uncertainty discussion of flood disaster system, a prediction model with time-varying multiple parameters had been set up. Based on the information entropy principle, the entropy-weight was used to parallel combine this model with the non-time-varying immune neural network model and the least-squares support vector machine model for mid- and long-term flood forecasting, analysis of the advantages and disadvantages of each single model and the practical significance of combined predictive modeling. Finally, taking the annual runoff forecasting of Yamadu Station in Xinjiang as a case study, the rationality, universality and reliability of the combined model have been confirmed.(4) Selected three major factors of life loss in flood disasters, including flood intensity, warning time, people's awareness of flood danger, categorized into 15 flood disaster scenarios of different combination. In accordance with domestic and foreign experience, the inference rule of life loss was determined. The cloud model was used to set up a quantitative model for the qualitative concept, and then with this rule the qualitative reasoning was performed for quantitative samples to be estimated, leading to the comprehensive qualitative and quantitative reasoning results(5) Refined the traditional whitening-weight function using the cloud model, which is a representation of both fuzziness and stochastic, and could take a bi-directional conversion between qualitative and quantitative. In this way, a grey-cloud clustering model is proposed for the synthetic ranking evaluation of flood disaster. The novel method is applied to evaluate the 45 flood disaster cases of several provinces in 1989～1999, and taking the amount of building collapse, affected area, number of victims, direct economic loss as evaluation indicators. The result shows that this method has much rationality and utility for flood disaster evaluation.