Flood disaster loss assessment method and simulation deduction system

Abstract

The invention relates to a flood disaster loss assessment method and a simulation deduction system, and belongs to the technical field of assessment analysis simulation, and the method comprises the steps: constructing and training a flood deduction neural network model; hydrological characteristic information is collected and monitored in real time, and the precipitation of a prediction area is calculated; flood deduction is carried out; setting loss evaluation parameters; and carrying out loss evaluation calculation. According to the invention, satellite remote sensing and a sensor are comprehensively utilized to monitor data, so that the problems of information integration complexity and insufficient real-time performance of collected information are solved; a neural network deduction model is established, the flood deduction capability is continuously optimized through deep self-learning, network parameters are adjusted and optimized according to land cover data, and the loss evaluation accuracy is improved; and a Markov chain method is adopted in precipitation prediction, so that the redundant data volume is relieved, and the efficiency of deduction calculation is improved.

Description

technical field [0001] The invention relates to a flood damage assessment method and a simulation deduction system, belonging to the technical field of assessment analysis simulation. Background technique [0002] Loss assessment of disasters after floods is a necessary work for flood control and disaster reduction. The damage caused by floods needs to be deduced from the development of floods. The accuracy of the deduction directly affects the accuracy of damage assessment. [0003] A large amount of regional precipitation in a short period of time is one of the direct causes of flood disasters, so precipitation forecasting is very important, and precipitation forecasting usually requires a large number of characteristic parameters and historical data over a period of time, which increases the complexity of the task and computing load. [0004] Changes in regional land cover have a relatively important impact on regional water storage capacity. The impact of similar preci...

AI Technical Summary

Problems solved by technology

[0005] Traditional multi-sensor data collection and the establishment of flood deduction calculation models require processing a large number of different types of data, which consumes resources and cannot guarantee the real-time and synchronization of data processing and utilization; while the acquisition of satellite remote sensing data is limited Influenced by factors such as the weather environment and satellite movement cycle, the reception of remote sensing data cannot cover all time periods, and the flood submersion range and the evolution process of historical information cannot be recorded during the time period when the information is received, and there are flaws in information prediction

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