Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models

This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning m...

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Veröffentlicht in:	The Science of the total environment Jg. 949; S. 175246
Hauptverfasser:	Wu, Juncheng, Dai, Qili, Song, Shaojie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.11.2024
Schlagworte:	air pollutants Air pollution atmospheric pressure BenMap cardiovascular diseases China decision support systems dewpoint environment health effects assessments Health impact assessment Machine learning Meteorological factors mortality prediction Prediction accuracy relative humidity respiratory tract diseases China Prediction accuracy Air pollution Health impact assessment BenMap Meteorological factors Machine learning
ISSN:	0048-9697, 1879-1026, 1879-1026
Online-Zugang:	Volltext
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