Addressing observational gaps in aerosol parameters using machine learning: Implications to aerosol radiative forcing-Reference-Cited by-同舟云学术

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Addressing observational gaps in aerosol parameters using machine learning: Implications to aerosol radiative forcing

Published:2024-09 Issue: Volume:333 Page:120661
ISSN:1352-2310
Container-title:Atmospheric Environment
language:en
Short-container-title:Atmospheric Environment

Author:

Parmar Mittal^ORCID,Vaishya Aditya^ORCID,Ojha Narendra^ORCID,Pandya Mehul R.,Girach Imran A.^ORCID

Funder

Ahmedabad University

Publisher

Elsevier BV

Reference86 articles.

1. Explaining individual predictions when features are dependent: more accurate approximations to Shapley values;Aas;Artif. Intell.,2021

2. Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India;Auffhammer;Proc. Natl. Acad. Sci. U.S.A.,2006

3. Machine learning in weather prediction and climate analyses--applications and perspectives;Bochenek;Atmosphere,2022

4. XGBoost with Python: Gradient Boosted Trees with XGBoost and Scikit-Learn;Brownlee,2016

5. Climate forcing by anthropogenic aerosols;Charlson;Science,1992

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