Spatiotemporal modeling of PM10 via committee method with in-situ and large scale information: Coupling of machine learning and statistical methods
Author:
Publisher
Elsevier BV
Subject
Atmospheric Science,Urban Studies,Environmental Science (miscellaneous),Geography, Planning and Development
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1. Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind;Environmental Pollution;2024-02
2. Predicting PM10 Concentrations Using Evolutionary Deep Neural Network and Satellite-Derived Aerosol Optical Depth;Mathematics;2023-09-30
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