空间时空臭氧 (O3) 污染建模的优化,使用集体机器模型学习与基于群体的元启发算法进行集体机器模型学习
Seyed Vahid Razavi-Termeh1, Abolghasem Sadeghi-Niaraki1, Armin Sorooshian2
1Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.
这项研究引入了一种新的时空模型,使用集体机器学习和Cuckoo搜索来预测臭氧 (O3) 污染. 精确的O3风险地图为公共卫生和环境政策提供了关键数据.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 臭氧 (O3) 污染对全球环境和公共卫生构成重大风险,影响呼吸道和心血管健康.
- 准确的时空建模对于评估和预测O3污染水平至关重要,以减轻其不利影响.
研究的目的:
- 开发一种新的时空模型,用于O3污染的评估和预测.
- 将集体机器学习算法与基于集群的元启发式优化算法集成在一起,以进行增强的O3建模.
主要方法:
- 利用来自伊朗 (2018-2022) 德黑兰的表面O3数据和14个环境因素.
- 采用随机森林 (RF) 作为基组机器学习模型.
- 使用Cuckoo Search (CS) 的元启发算法优化了射频模型.
主要成果:
- 在不同季节的O3风险地图预测中实现了高准确性:95.2% (秋季),97% (春季),96.7% (夏季) 和95.7% (冬季).
- 接收器运行特征 (ROC) 曲线评估证实了强大的模型性能.
结论:
- 新的综合模型有效地预测了高精度的O3污染.
- 调查结果为决策者和公共卫生官员提供了可操作的见解,以解决O3对健康和环境的影响.
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