基于VGAE-RF模型的城市紧急避难所的选址和预测
Yong Wang1,2, Yaoyao Han3, An Luo4
1School of Geomatics, Anhui University of Science and Technology, Huainan, 232001, China.
Scientific reports
|June 22, 2024
概括
本研究引入了一种新的人工智能模型 (VGAE-RF),用于选择紧急庇护所的位置,通过考虑空间关系来改进传统方法. 该模型在选择灾难准备的最佳地点方面表现出卓越的可靠性.
科学领域:
- 城市规划 城市规划
- 灾害管理 灾害管理
- 人工智能的人工智能
背景情况:
- 加快的城市发展和自然灾害的增加需要改进紧急庇护计划.
- 传统的选择庇护所地点的多标准决策方法是主观的,缺乏数据支持.
- 现有的人工智能方法在选址时经常忽视关键的空间关系.
研究的目的:
- 开发一个先进的紧急避难所选址模型.
- 整合空间关系和地理信息,以便更准确地预测地点.
- 解决传统和现有的基于人工智能的选址方法的局限性.
主要方法:
- 开发了一种新型模型,即随机森林变量图形自编码器 (VGAE-RF).
- 使用网络地理信息构建了一个城市空间拓图.
- 该模型捕获了地理单位合的潜在特征,并集成了显式/隐性特征.
主要成果:
- VGAE-RF模型在紧急避难所选址方面表现出卓越的可靠性.
- 使用混矩阵,ROC曲线和不平衡指数的评估证实了模型的性能.
- 该模型有效地考虑了空间语义关联,以改善预测.
结论:
- 拟议的VGAE-RF模型为紧急避难所规划提供了一个强大的数据驱动解决方案.
- 纳入空间关系显著提高了选址模型的准确性.
- 这种方法为城市防灾准备提供了更可靠的策略.
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