Environmental Science
基于多源地球观测数据的城市PM2.5和PM10 100米网格级估算的空间稳健且可解释机器学习框架
A spatially robust and explainable machine learning framework for 100 m grid-level estimation of urban PM2.5 and PM10 using multi-source earth observation dataA method for estimating air quality using machine learning on multi-source earth observation data at a 100 m grid scale. / 一种利用机器学习在100米网格尺度上基于多源地球观测数据估算空气质量的方法。
- Knowledge type
- 实际应用
- First proposed or discovered
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- 农场主
- Last updated
- 2026-11-14 23:05:19
Sub-knowledge points
Spatial robustnessExplainabilityMulti-source earth observation data integration
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Main parameters and performance indicators
PM2.5: Fine particulate matter concentrationPM10: Coarse particulate matter concentrationGrid size: 100 metersData source: Multi-source earth observation data
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Related literature
ranked by community starsA spatially robust and explainable machine learning framework for 100 m grid-level estimation of urban PM2.5 and PM10 using multi-source earth observation dataOpen source
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1 recordsRecord title: A spatially robust and explainable machine learning framework for 100 m grid-level estimation of urban PM2.5 and PM10 using multi-source earth observation data.
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