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探索有效的方式,以增加可靠的阳性样本,以机器学习为基础的城市淹水易感性评估

Xianzhe Tang¹, Zhanyu Wu², Wei Liu³

¹Guangdong Province Key Laboratory for Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China; College of Natural Resources and Environment, Joint Institute for Environment & Education, South China Agricultural University, Guangzhou 510642, China.

Journal of environmental management

|August 11, 2023

概括

优化城市浸水易感模型需要可靠的阳性样本. 优化种子传播算法 (OSSA) 有效模拟水流,以生成准确的样本,优于合成少数人过量采样技术 (SMOTE).

科学领域:

环境科学环境科学
地理信息科学地理信息科学
城市规划城市规划

背景情况:

机器学习模型的城市水淹没易感性往往面临阶级不平衡,积极的样本有限.
提高培训数据质量对于提高模型性能和预测积水的准确性至关重要.
现有的过量采样技术可能无法充分反映水浸的物理机制.

研究的目的:

调查有效的方法,以增加可靠的阳性样本在城市浸水易感性研究.
为了比较过量采样 (SMOTE) 和物理模拟 (OSSA) 样本生成方法的性能.
评估不同样本生成技术对分类器性能和浸水易感地图 (WSM) 准确性的影响.

主要方法:

采用合成少数人过量采样技术 (SMOTE) 和优化种子扩散算法 (OSSA) 来生成合成阳性样本.
通过使用八个空间变量,对深的水淹没情况进行了实例研究.
使用原始样本,SMOTE生成的样本和OSSA生成的样本比较分类器性能和WSM准确性.

主要成果:

用SMOTE生成的样本进行培训的分类器的表现比使用原始样本进行培训的分类器要差.
与原始样本相比,OSSA显著提高了训练有素的分类人员的表现.
SMOTE没有提高WSM的准确性,而OSSA显著提高了WSM的准确性.

结论:

由于基于特征空间的方法,SMOTE在深的浸水分析中无法产生有效的阳性样本.
通过模拟水流机制,OSSA有效地产生可靠的阳性样本,增强浸水易感模型.
像OSSA这样的物理模拟方法优于数据驱动的过量采样,可以提高浸水易感性评估的准确性.

关键词:

不平衡的阶级是不平衡的.机器学习机器学习在OSSA的OSSA.在SMOTE中使用.城市的水淹没.

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