多方数据融合的山洪灾害模型应用研究

山洪灾害预测是应急管理工作中的一个重要部分,如何实时、准确得到灾害数据并进行关联分析,是灾害调查中的一个难点。研究了洪涝与地质灾害多方数据的感知与融合,通过结合多方数据融合组件、天-空-地的立体网格体系、数据分析引擎与算法模型、数据同化技术、核心专业模型以及人工智能分析等关键技术,搭建出轻量版山洪、泥石流、山体滑坡大数据平台。平台囊括了山洪水情预报模型和小型可视化灾情信息系统,可灵活呈现监测分析结果;耦合二维、三维GIS,实现微观-局部-整体、过去-现在-未来的多方位、高频自动监测-遥感影像解译-流域水文模拟、多视角、多维度展示。此平台内置的数据分析引擎与算法模型通过数值计算形成虚拟监测数据,采用熵值法、热点技术、机器算法等对水位、流量、流速等数据构建统计预测模型,并实现多源数据验证,有助于大幅度减少监测点与运维成本。平台还可将灾害数据关联分析平台与人工指挥决策系统打通,为预测、决策、指挥提供依据。

Research on the application of flood model based on data fusion

Flooding disaster prediction is an important part of emergency management;How to get disaster data in real time and accurately and carry out correlation analysis is a difficult point in disaster investigation. This paper is committed to the research on the perception and fusion of multi-source data of flood and geological disasters. Through the fusion of multi-source data, data analysis engine and algorithm model, data assimilation technology, core professional model and artificial intelligence analysis and other key technologies, a lightweight big data platform is built. The platform includes a flood prediction model and a small visual disaster information system, which can flexibly present the monitoring and analysis results. The data analysis system and algorithm model of this platform form virtual monitoring data through numerical calculation. Entropy method, hot spot technology and machine learning method are used to build up statistical models for water level, flow rate and other data. And the multi-source data could be the parallel verification method, which is helpful to greatly reduce the monitoring points and operation and maintenance costs. This platform can also connect the disaster data association analysis platform with the manual command and decision-making system, and provide the basis for prediction, decision-making and command.