在阿曼,利用机器学习改进基于指数的沿海脆弱性评估
Malik Al-Wardy1, Erfan Zarei1, Mohammad Reza Nikoo2
1Center for Environmental Studies and Research, Sultan Qaboos University, Muscat, Oman.
The Science of the total environment
|April 10, 2025
概括
这项研究将机器学习与基于索引的方法相结合,以加强沿海脆弱性评估. 机器学习模型为了解沿海危险及其影响提供了更灵活的方法.
科学领域:
- 环境科学 环境科学
- 地理空间分析的研究.
- 机器学习应用 机器学习应用
背景情况:
- 沿海脆弱性评估对于了解环境危害影响至关重要.
- 传统的基于索引的方法往往无法有效衡量参数.
- 由于其环境意义,阿曼的海岸线需要强大的脆弱性测绘.
研究的目的:
- 将机器学习模型与基于指数的方法集成在一起,以改进沿海脆弱性指数 (CVI) 的计算.
- 为了比较机器学习 (随机森林,XGBoost) 与传统方法 (AHP,Shannon's Entropy) 的CVI映射的性能.
- 确定关键的脆弱性参数及其在阿曼海岸线的分布.
主要方法:
- 使用粒子集群优化来调整随机森林和XGBoost模型.
- 采用特征重要性分析来确定CVI计算的参数权重.
- 将机器学习衍生的CVI图与分析层次过程 (AHP) 和Shannon's Entropy的CVI图进行比较.
主要成果:
- 地质形态是最有影响力的参数,在许多地区表明中等到非常高的脆弱性.
- 高度和斜率显示,阿曼大部分海岸线的脆弱性非常低.
- 在不同方法中观察到CVI结果的显著差异,机器学习提供了更灵活的方法.
结论:
- 将机器学习与基于索引的方法集成,为沿海脆弱性评估提供了更细致和灵活的方法.
- 不同的方法优先考虑不同的沿海因素,影响CVI结果.
- 该研究提供了阿曼海岸线的全面CVI地图,突出了令人担忧的地区.
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