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在中国建设更安全,更有弹性的城市:一种新的方法,使用动态的非均的灰色模型,以数据驱动的决策
Jian Liu1,2, Ye He1, Rui Feng1,3
1School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, People's Republic of China.
PloS one
|December 5, 2024
概括
本研究介绍了一种动态非同质灰色模型 (DNMGM(1,1) 用于预测中国快速城市化中的建筑物和交通事故死亡人数. 该模型显示高精度,支持弹性城市发展和安全规划.
科学领域:
- 城市规划和减少灾害风险
- 城市安全的预测建模.
- 在快速城市化的环境中实现可持续发展.
背景情况:
- 中国的快速城市化需要加强城市的性,以实现可持续发展和居民安全.
- 建筑安全是城市恢复力的关键组成部分,需要准确的风险评估和缓解策略.
- 现有的预测模型经常与实时数据集成和波动趋势作斗争.
研究的目的:
- 引入和验证动态非均质灰色模型 (DNMGM(1,1) 用于模拟和预测城市死亡数据.
- 评估DNMGM的预测准确性,对建筑物和交通事故死亡人数.
- 为城市规划者和政策制定者提供数据驱动的决策工具,以提高城市的弹性.
主要方法:
- 动态非同质灰色模型 (DNMGM(1,1) 的应用,用于时间序列预测.
- 使用DNMGM(1,1) 模型模拟建筑死亡人数.
- 使用交通事故死亡人数数据集验证模型的性能.
主要成果:
- DNMGM(1,1) 模型显示出高预测准确度,平均相对误差为9.26% (建筑) 和7.29% (交通事故),使用数据集大小为6.
- 该模型能够实时整合新数据,从而在数据波动和新趋势的情况下进行准确的预测.
- 在城市死亡数据的预测准确性方面,DNMGM(1,1) 超过了传统的灰色模型.
结论:
- DNMGM(1,1) 模型提供了一个强大的工具,通过准确的死亡预测来增强城市的弹性.
- 这种预测能力支持明智的规划和资源配置,为更安全,更弹性的城市提供支持.
- 该研究为制定政策框架提供了基础,以促进中国及其他地区的城市发展.
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