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一个集成机器学习模型,以基于有限的监督经验来发现潜在的危险废物非法倾倒地点
Jinghua Geng1, Yimeng Ding1, Wenjun Xie1
1State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023 China.
Fundamental research
|August 19, 2024
概括
一个新的机器学习模型使用有限的历史数据识别危险废物 (HW) 非法倾倒场. 这种方法有效地选高风险地区,帮助环境保护工作.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 工业化和城市化产生了大量的危险废物,导致了广泛的非法倾倒.
- 非法倾倒污染了土壤和地下水,特别是在监管监督较弱的地区.
- 识别隐藏的非法倾倒场是环境监督的一个重大挑战.
研究的目的:
- 开发一种机器学习模型,使用有限的历史数据选潜在的危险废物非法倾倒场.
- 为应对隐形垃圾填埋场的挑战,改善环境管理.
主要方法:
- 开发了一个基于积极未标记 (PU) 学习的新型机器学习模型.
- 采用集体方法,从有限的历史案例中代地挖掘特征.
- 一个基于森林的随机PU模型被验证用于预测高风险地区.
主要成果:
- 基于森林的随机PU模型准确地确定了非法倾销的高风险地区.
- 预测高风险地区的前30%包括68.1%的新报告的非法倾销案件.
- 该模型在预测潜在的危险废物倾倒场所方面表现出可靠性.
结论:
- 拟议的机器学习框架有效地选潜在的危险废物非法倾倒场.
- 这种新的方法为环境管理挑战提供了一个有希望的解决方案,数据有限.
- 该框架可以应用于其他场景,涉及许多未知的样本和有限的先前经验.
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