一种洪涝灾害事件信息建模方法

我国洪涝灾害频发且危害巨大,为有效防灾、减灾和救灾,需建立洪涝灾害事件信息模型,表示和共享洪涝灾害信息。然而,目前事件信息模型主要是表示静态信息,缺乏对洪涝灾害事件动态过程的表示。本文从观测角度出发,建立顾及洪涝灾害事件阶段和动态过程的洪涝灾害事件信息模型。围绕洪涝灾害事件信息模型,提出了10类需建模的元素,并以MOF（Meta Object Facility）建模框架构建了十元组模型,通过映射方法实现模型编码方案与事件模式语言的映射。最后,以2010年梁子湖洪涝灾害模拟为例,开展了模型分阶段建立过程实验。结果表明,本文提出的模型能表示洪涝灾害事件的动态信息和实现洪涝灾害事件的动态建模。

A Method of Modelling Flood Event

Flood is a frequently occurred disaster and it causes great harm in China. In order to effectively prevent, reduce and relief disaster, it is necessary to establish a flood event information model to represent and share flood information. By far, many scholars have studied a variety of event information models. However, the existed event information models mainly represent static information, and they are lack of the capability to handle dynamic process information. To solve this problem, a flood event model is proposed in this paper based on the flood emergence management stages and the dynamic process from the perspective of observation. To build up the model, this paper firstly describes the modelling considerations, including flood phases, observation, and ten types of primary elements for modeling. Then, we construct the flood event model as a ten tuple model based on MOF modelling framework with four layers, and encode the model with a method that maps the elements of the model to elements of the Event Pattern Markup Language (EML). Finally, we simulated an experiment for a flood case occurred in the Liangzi Lake in 2010 as an example to test the proposed model. In the experiment, the building process and the results of the proposed model at different emergence management stages are detailed. The experiment results show that the proposed model has following characteristics: (1) it establishes a model from the perspective of observation, since observation plays a decisive role in real-time disaster information acquisition. (2) The flood model was established by four emergence management stages, and the main tasks of each stage are different. (3) The flood model was modelled dynamically. The proposed ten tuple model itself is not dynamic, but the establishing process and its represented information are dynamic. The modelling process is modelled on real-time data streams, and it dynamically updates time-series data. The time series data in this model are generally the latest observations, which reveal the dynamic information of current flood. (4) The model is extensible and adaptable to ensure the suitability. As a conclusion, the model can represent flooding events and model dynamic flood information effectively, as well as provide a good approach to represent and share flood information.