| Currently, with the aggravation of global floods disaster, non-structural measures against floods have drawn more attention. Under this background, it has practical sense to develop a disaster evaluation system in flood control decision-making. With the analysis of demands of current disaster evaluation we designed a function framework for the evaluation system. Also, computer technology, artificial intelligent technology, visual simulation technology, and even mathematics, management, decision-making, and other ologies have been used to build a complete set of evaluation models. Our work has established foundation for achieving scientific, systematic, and rational disaster evaluation.First, we put forward the three-layer architecture of the decision support system (DSS) for flood control, and make detailed design separately. Hereinto, the interface layer implements friendly interaction between system and analysers;the application layer uses data and models to implement the analysis of information requirements in every part, it is the hard-core of the system;the foundation layer provides information and technology support for the system, it includes database (storing space data and attribute data), model-base and knowledge-base.Second, based on the analysis of system function requirements, we put forward a disaster evaluation system which combines three function modules: flood-inundated simulation, damage calculation, and disaster classification. Flood-inundated simulation repeats the flood-inundated process and obtains disaster indices such as flood-inundated area and water-deep distribution. Damage calculation module evaluates the destruction caused by floods through rational analysis while disaster classification module through qualitative analysis. Since these three modules evaluate disaster step by step, they can provide a complete and scientific evaluation.After that, implementation of 3D visual simulation (3DVS) of flood-inundated process can provide real-time flood behaviour character for calculating damage and measuring disaster grade. Firstly, we use TIN algorithm and GRID algorithm to build 3D digital terrain from scattered elevation spot;then we use improved grid spread algorithm tosimulate flood-inundated process and estimate flood-inundated area and water-deep distribution under certain condition of flood water levels or flood volumes;Finally, we use Web-based 3D visualization tool (Java 3D) to draw flood hazard maps, and demonstrate flood-inundated process dynamically under different conditions.Next, we propose a new method to calculate direct flood damage. Based on systematic theory, we analyze the effect factors of flood damage, and build rapid damage evaluation model. The input of the model is damage effect factors while the output is direct flood damage. Compared with losing-rate model, the proposed model is easy to use with more factors considered, so it has more comprehensive applicability and better evaluation performance. In addition, we introduce estimation methods for three indirect flood damage, they are: enterprise halt damage, domain associated loss, and disaster reduction input. We define the flood damage degree as a comparative damage index which is used to estimate comparative damage in floods and its restriction to national economy development.Then, in order to overcome the limitation of standard genetic algorithm (SGA), such as poor local-search performance, immaturity convergence, etc., we propose an immune genetic algorithm (IGA) based neural network model (IGA-NN) for flood disaster classification. Also, we provide design methods and optimization procedures of IGA-NN. With the help of practical applications, we compare IGA-NN with the neural network optimized by SGA. As a result, IGA-NN can reflect the relationship between indices and disaster grade. So it can be used in disaster classification, and has faster response speed and higher evaluation precision.Finally, we conclude the thesis, and provide the direction for further studies.
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