基于信息价值和机器学习合方法的碎片流易感性评估:从可持续发展的角度来看
Jiasheng Cao1, Shengwu Qin2, Jingyu Yao1
1College of Construction Engineering, Jilin University, 938, Ximinzhu Road, Changchun, China.
概括
这项研究优化了非灾害数据采样,以评估碎片流的易感性. 结合信息价值-人工神经网络 (IV-ANN) 模型实现了高精度,提高了经济效益并降低了预防灾害的成本.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 碎片流灾难易感性评估对于减轻损失至关重要.
- 机器学习 (ML) 模型被广泛使用,但在非灾难数据采样方面面临挑战,影响准确性.
- 优化非灾难数据选择是提高ML模型在易感性评估中的性能的关键.
研究的目的:
- 优化非灾难数据采样方法以基于ML的碎片流感受性评估.
- 提出和评估一种新的易感性预测模型,将信息价值 (IV) 与人工神经网络 (ANN) 和后勤回归 (LR) 结合起来.
- 为了生成一个准确的碎片流易感分布地图的永吉县,中国.
主要方法:
- 优化了ML模型的非灾难数据集的采样.
- 开发了一种混合模型,将信息价值 (IV) 与人工神经网络 (ANN) 和后勤回归 (LR) 集成在一起.
- 使用曲线下的面积 (AUC),信息获取比率 (IGR) 和灾难点验证来评估模型性能.
主要成果:
- 雨量和地形被确定为碎片流发生的决定性因素.
- 拟议的IV-ANN模型表现出卓越的准确性,AUC为0.968.
- 与传统的ML模型相比,合模型增加了约25%的经济效益,并减少了约8%的灾害预防成本.
结论:
- 优化的采样和IV-ANN模型显著提高了碎片流感受性评估的准确性.
- 这些发现为有针对性的灾害预防和控制战略提供了可靠的基础.
- 建议包括建立监测系统和信息平台,以支持可持续发展和灾害管理.
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