Study And Implement Of Disaster Prognostication Model And Property Loss Evaluation Model

In order to prevent natural disasters and reduce the loss of property insurance, it is necessary to set up a scientific disaster prognostication model and a property loss evaluation model according to the need of current and future disaster prevention and loss reduction of property insurance. The technique of disaster prevention and loss reduction of property insurance is an integration of remote sensing, geographic information system and global position system. It collects effective disaster information, according to which we perform disaster prediction and loss evaluation, and instruct insurance companies to do the work of disaster prevention and loss reduction.Our goal is to give effective consultation to insurance companies in their work of disaster prevention and loss reduction. This thesis reviews the current research status of disaster prognostication and property loss evaluation. It analyzes the existing prognostication and evaluation methods, and proposes a disaster prognostication method based on DEM (Digital Elevation Model), and a property loss evaluation method based on RBF (Radial Basis Function) neural network. The disaster prognostication method consists of disaster primary prediction part and disaster correction part. The primary prediction part considers and analyzes various factors which affect the situation of the disaster, to get the flooding area, depth and duration. We use neural network model to implement correction part, train it using the samples of history disaster data, and correct the computing result of the former, then get the ideal result, which improves the prognostication precision. The property loss evaluation method targets insurance item as evaluation object. By using the collected data effectively, it builds a model using the method of RBF neural network, and this model is used to evaluate the property loss. This project designs and implements the disaster prognostication model and the property loss evaluation model, and integrates them into a prototype system of disaster prevention and loss reduction of property insurance. The models were applied in Shenzhen as a test, and showed a satisfactory result.