| 基于大数据的地震多发区域破坏程度估计模型设计 |
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| 引用本文： | 成桂兰.基于大数据的地震多发区域破坏程度估计模型设计[J].西北地震学报,2018,40(3):604-611. |
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| 作者姓名： | 成桂兰 |
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| 作者单位： | 烟台职业学院信息工程系 |
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| 基金项目： | 山东省科技发展项目(2014GGX3030) |
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| 摘 要： | 传统基于GIS的地震破坏程度估计模型,对于大数据的分析和处理性能差,评估效果不够理想,所以要设计基于大数据的地震多发区域破坏程度估计模型。塑造的模型体系结构由数据服务层、业务模型层、应用展现层构成。模型由基础数据控制模块、地震危险性模块、结构破坏性模块、损失评估模块、决策控制模块、文档控制模块五大功能结构构成,设计直接经济损失模块的逻辑流程和页面展示结果。模块采用随机权神经网络实现大数据环境下地震灾害破坏程度快速评估。实验结果说明,所设计模型实现了大数据环境下地震多发区域破坏程度的有效评估,具有较高的评估效率和精度。
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| 关 键 词： | 大数据 地震多发区域 破坏程度 估计 模型 随机权 神经网络 |
| 收稿时间： | 2017/8/20 0:00:00 |
Design of a Damage Degree Estimation Model in Earthquake-prone Areas Based on Big Data |
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| CHENG Guilan.Design of a Damage Degree Estimation Model in Earthquake-prone Areas Based on Big Data[J].Northwestern Seismological Journal,2018,40(3):604-611. |
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| Authors: | CHENG Guilan |
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| Institution: | Department of Information Engineering, Yantai Vocation College, Yantai 264670, Shandong, China |
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| Abstract: | The traditional earthquake damage degree estimation models based on geographic information system are inefficient in analyzing and processing big data, and they result in a non-ideal evaluation. Therefore, considering a big data environment, a damage degree estimation model in earthquake-prone areas is designed in the study. The model structure is composed of data service layer, business model layer, and application display layer. The model is composed of six functional structures: basic data control module, seismic hazard module, structural failure module, loss assessment module, decision control module, and document control module. The logical process and page display result of direct economic loss module are designed in the model. The module utilizes a random weight neural network to rapidly assess earthquake damage degree in a big data environment. The experimental results show that the designed model can effectively and accurately evaluate the destruction degree in earthquake-prone areas under a big data environment. |
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| Keywords: | big data earthquake-prone area damage degree estimation model random weight neural network |
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