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使用XGBoost和memetic编程来识别沉积物塑料污染的热点
Desmond N Shiwomeh1, Sameh A Kantoush2, Mohamed Saber2
1Department of Urban Management, Graduate School of Engineering, Kyoto University, Kyoto, 612-8133, Japan; Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, 611-0011, Japan.
Environmental pollution (Barking, Essex : 1987)
|October 31, 2025
概括
塑料污染热点更多地受到景观特征的影响,而不是城市特征. 机器学习模型有效地识别这些领域,帮助有针对性的塑料废物管理策略.
科学领域:
- 环境科学 环境科学
- 地理空间分析是什么
- 机器学习 机器学习
背景情况:
- 全球不到10%的塑料垃圾被回收利用,导致严重的环境污染.
- 塑料热点 (巨型塑料) 在全球城市环境中越来越令人担忧.
- 了解影响塑料积累的因素对于有效缓解至关重要.
研究的目的:
- 调查地形,水文和城市因素对喀麦隆德塑料热点分布的影响.
- 为了比较Extreme Gradient Boosting (XGBoost) 和Memetic Programming (MP) 算法在识别塑料热点方面的性能.
- 评估空间显式参数对塑料污染的预测能力.
主要方法:
- 采用极端梯度提升 (XGBoost) 和记忆编程 (MP) 机器学习算法.
- 利用了12个空间显式参数,包括地形,水文和城市变量.
- 使用精度,灵敏度,特异性,PPV和NPV评估模型性能.
主要成果:
- 地形和水文因素对塑料热点形成的影响比城市变量更大.
- 人口密度,道路近距离和废物管理基础设施是人类热点的关键城市预测因素.
- 组合参数显著改善了模型性能,达到超过75%的准确性.
- 记忆编程 (MP) 比XGBoost表现出更好的概括性,而XGBoost表现出过度匹配.
结论:
- 空间显式机器学习模型是减轻塑料污染的有价值工具.
- 通过先进的建模识别塑料热点,可以指导有针对性的废物管理干预.
- 环境和城市因素相互作用,在城市化盆地塑料污染模式的形成.
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