Method for predicting dynamic risk and vulnerability under fine dimension

Abstract

The invention relates to a method for predicting dynamic risk and vulnerability at fine scale and belongs to the scientific field of global information. The method is mainly characterized in that an optimized Bayesian network is searched from multi-source heterogeneous spatiotemporal data on the basis of a grid format with certain resolution at fine scale; domain knowledge is combined to improve the network; therefore, the uncertain estimation of disaster risk and the vulnerability, namely probability estimation, is carried out. In the method, a nuclear density method is put forward to train a sample according to a sample derivative grid; an optimized discretization method is put forward to discretize continuous variables so as to provide discrete state space input for the network; a simulated annealing optimization algorithm is adopted to search an optimized network structure; and a method of accurate reasoning combined with approximate reasoning to predict the probabilities of risk and the vulnerability is adopted. The method provided by the invention can position the positions of the disaster risk and the vulnerability in real time at the fine spatial scale, estimate the spatial distribution of the risk probability and has important theoretical significance and practical value for improving the effects on the reduction and relief of disaster and building an intelligent public emergency pre-warning system by the state.

Description

technical field [0001] The invention discloses a fine-scale dynamic risk and vulnerability prediction method, which is used for prediction of sudden public disasters and belongs to the technical field of earth information science. Background technique [0002] Monitoring and early warning of sudden public disasters plays an important role in national disaster prevention and mitigation. Accurate and timely early warning will greatly reduce the loss of life and property and improve the efficiency of disaster prevention and mitigation. Establishing a timely and accurate monitoring and early warning system has always been the focus of national disaster prevention and mitigation; at the same time, due to the suddenness, randomness, diversity and uncertainty of influencing factors of sudden public disasters, accurate and timely forecasting is relatively difficult However, risk analysis is a key technology in monitoring and early warning, and its research and realization of related...

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Problems solved by technology

[0006] (2) Another method is based on the analysis results at the mesoscale or large scale, which lacks the risk probability and vulnerability estimation at the fine scale, and it is more difficult to carry out the spatial positioning at the fine scale, and the positioning deviation of the prediction results is large (Wang Yanyan, 2002 , Review of Flood Disaster Loss Assessment Models at Different Scales, Water Conservancy Development Research, Volume 2, Issue 12);

[0008] (4) Some methods lack the ability to flexibly conduct dynamic probabilistic reasoning on risks based on real-time data, and the real-time prediction function is relatively lacking (William, J.P., and Arthur, A., 1982, Natural Hazard Risk Assessment and Public Policy, New York: Springer-Verlag New York Inc.)

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