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S1 Text - Machine learning-based assessment of aerosol optical depth over Ghana, West Africa using MODIS satellite data

Domain:

environment and energygeospatial

Record type:

paper
Creator:
JesJefMarCal
Host:avatar

Supplementary Information. Contains:

– Table A: Physical relevance of meteorological variables used in AOD prediction.

– Table B: Missing time steps identified in the TERRA dataset.

– Equations A–F: Mathematical formulations for regression, ANN, and evaluation metrics (RMSE, MAE, R2, KGE).

– Fig A: Architecture of the Artificial Neural Network (ANN).

– Fig B: Seasonal distribution of AOD from AQUA.

– Fig C: Seasonal distribution of AOD from TERRA.

– Table A: Physical relevance of meteorological variables used in AOD prediction.

– Table B: Missing time steps identified in the TERRA dataset.

– Equations A–F: Mathematical formulations for regression, ANN, and evaluation metrics (RMSE, MAE, R2, KGE).

– Fig A: Architecture of the Artificial Neural Network (ANN).

– Fig B: Seasonal distribution of AOD from AQUA.

– Fig C: Seasonal distribution of AOD from TERRA.

Visit

figshare.com

Tags

MicrobiologyBiotechnologyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedspatiotemporal distribution patternssatellite remote sensingregion &# 8217multiple linear regression+36

Licenses

CC BY 4.0

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