机器学习支持城市环境管理中的公民科学
Emily J Yang1,2, Julian Fulton1, Swabinash Swarnaraja1
1California State University Sacramento, 6000 J St. Sacramento, CA 95819-6001, USA.
Heliyon
|December 7, 2023
概括
机器学习 (ML) 可以提高环境管理的公民科学 (CS) 数据质量. 将ML与CS整合在一起,可提供对城市垃圾的可靠评估,改善雨水计划的监管合规性.
科学领域:
- 环境科学环境科学
- 计算机科学 计算机科学
- 环境管理环境管理
背景情况:
- 机器学习 (ML) 和公民科学 (CS) 对环境管理至关重要.
- 整合ML和CS提供了机遇和挑战,特别是在数据有效性和质量保证方面.
- 城市垃圾是一个重大的环境挑战,需要有效的监测和管理策略.
研究的目的:
- 展示ML如何通过为城市垃圾评估提供质量保证来支持CS.
- 评估ML模型在预测监管指标中的有效性,通常由专家评估.
- 探索在环境管理中整合ML和CS的协同潜力.
主要方法:
- 通过CS收集的定量数据被用于训练和测试五个ML模型.
- 机器学习模型预测了定性,特定于地点的多类"垃圾排放指数"得分.
- 用准确性,精度,回忆和F-1分数来评估模型性能.
主要成果:
- XGBoost ML模型获得了最高的性能,准确度,精度,回忆和F-1的得分为0.98.
- ML模型显示出可靠的能力来补充城市垃圾的CS评估.
- 该研究证实了ML在监管框架内增强质量保证的潜力.
结论:
- ML和CS的整合为环境管理提供了显著的协同效应,特别是在城市垃圾监测方面.
- ML为质量保证提供了一个强大的工具,在监管环境中提高了CS数据的可靠性.
- 这种综合方法在城市垃圾之外的其他环境管理领域也有广泛的适用性.
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