机器学习和地缘空间分析的方法集成用于PM10污染映射
Kalid Hassen Yasin1, Muaz Ismael Yasin2, Anteneh Derribew Iguala3
1Geo-Information Science Program, School of Geography and Environmental Studies, Haramaya University, P.O. Box 138, 3220 Dire Dawa, Ethiopia.
MethodsX
|May 7, 2025
概括
随机森林 (RF) 机器学习准确地模拟了空气污染 (PM10) 的空间分布,表现优于K-最近邻居 (KNN) 和天真海湾 (NB). 这通过可靠的空气质量监测来加强公共卫生干预.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 准确的空间建模对于减轻空气污染和公共卫生干预至关重要.
- 传统方法与复杂的预测剂-污染物相互作用作斗争,突出了对先进技术的需求.
- 机器学习 (ML) 为环境数据中的非线性关系建模提供了卓越的能力.
研究的目的:
- 为了比较三个ML算法的性能:随机森林 (RF),K-最近邻居 (KNN) 和天真贝叶斯 (NB).
- 通过使用各种环境共变量,评估它们在PM10度空间建模中的有效性.
- 确定最可靠的算法,以进行可靠的空气质量监测和污染热点映射.
主要方法:
- 利用来自11个监测站的年度PM10数据以及大气,城市和地形共变量.
- 实施严格的数据预处理,包括处理缺失/异常数据和规范化.
- 应用并验证了RF,KNN和NB算法与超参数调和交叉验证.
主要成果:
- 随机森林 (RF) 以94%的平衡精度和97%的特异性表现出卓越的性能.
- 射频显著优于KNN (92%准确率) 和天真贝叶斯 (NB) (89%准确率).
- 与KNN和NB不同,RF有效地捕捉了空间异质性和复杂的变量相互作用.
结论:
- 随机森林是一个可靠的算法,用于强大的空气质量监测和空间PM10建模.
- 研究结果支持实施可扩展的污染预测系统,即使在资源有限的环境中.
- 承认环境应用中复杂的ML模型所带来的解释性挑战.
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