以人工智能为基础的模型用于选择地震应急庇护所的地点
Amirmasoud Amiran1, Behrouz Behnam2, Sanaz Seyedin3
1Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran.
Scientific reports
|November 23, 2024
概括
这项研究开发了一个人工智能模型来绘制地震应急庇护所的地图,比传统方法提高了准确性. 像SVM和ANN这样的机器学习算法为灾害管理规划人员提供了可靠的工具.
科学领域:
- 灾害管理 灾害管理
- 人工智能的人工智能
- 地理信息系统 (GIS) 是一个地理信息系统.
背景情况:
- 传统的紧急庇护所位置依赖于专家问卷,这些问卷面临全球准确性批评.
- 提高避难所选址的速度和准确性对于有效应对灾害至关重要.
- 人工智能和机器学习为优化紧急避难所安置提供了潜在的解决方案.
研究的目的:
- 开发基于人工智能的模型,用于绘制地震易发地区潜在的紧急避难所的地图.
- 与现有的方法相比,提高紧急避难所选址的准确性和效率.
- 创建适用于具有相似地震标准的不同地区的通用模型.
主要方法:
- 利用了机器学习算法,包括支持向量机 (SVM),K-最近邻居 (KNN),后勤回归 (LR),高斯过程分类器 (GPC) 和人工神经网络 (ANN).
- 使用旧金山现有的紧急庇护地图训练模型.
- 根据F1分数评估算法性能,以准确选择避难地点.
主要成果:
- 大多数算法 (SVM,KNN,GPC,ANN) 在识别潜在的紧急避难地点时获得了高F1分,在0.7到1.0之间.
- 开发的AI模型证明了能够自动生成潜在的紧急庇护地图的能力.
- 该模型的有效性使用旧金山数据进行了验证,显示了概括的潜力.
结论:
- 基于人工智能的模型为地震易发地区的灾害管理规划人员提供了可靠和准确的工具.
- 机器学习显著提高了紧急避难所选址的速度和准确性.
- 开发的模型可以被通用化,以协助在类似的城市环境中为受地震影响的人口规划紧急避难所.
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