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The impact of the COVID-19 pandemic on air pollution: A global assessment using machine learning techniques
In response to the COVID-19 pandemic, most countries implemented public health ordinances that re...
Jasper S. Wijnands, Kerry A. Nice, Sachith Seneviratne, Jason Thompson, Mark Stevenson | Journal: Atmospheric Pollution Research | Volume: 13 | Year: 2022 | doi: https://doi.org/10.1016/j.apr.2022.101438
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Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry
Wind retrieval parameters, i.e. quality indicators and the two-dimensional variational ambiguity ...
Xingou Xu, Ad Stoffelen
| Journal: Atmospheric Measurement Techniques | Volume: 14 | Year: 2021 | doi: https://doi.org/10.5194/amt-14-7435-2021
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Simulating and Analysing Seabird Flyways: An Approach Combining Least-Cost Path Modelling and Machine Learning
Aim
We develop and test a cost framework to simulate the flyways of migratory seabirds, consider...
Nomikos Skyllas, Mo A Verhoeven, Maarten JJE Loonen, Richard Bintanja | Journal: Global Ecology and Biogeography | Volume: 34 | Year: 2025 | First page: e70016 | doi: https://doi.org/10.1111/geb.70016
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Advancing Data Quality Assurance with Machine Learning: A Case Study on Wind Vane Stalling Detection
High-quality observational datasets are essential for climate research and models, but validating...
V. S. de Feiter, J. M. I. Strickland, I. Garcia-Marti | Journal: Atmosphere, 16(2) | Volume: 129 | Year: 2025 | First page: 1 | Last page: 23 | doi: https://doi.org/10.3390/atmos16020129
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Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models
Climate change has a large influence on the occurrence of extreme hydrological events. However, r...
SM Hauswirth, K van der Wiel, MFP Bierkens, V Beijk, N Wanders | Journal: Frontiers in Water | Volume: 5 | Year: 2023 | First page: 11008108 | doi: 10.3389/frwa.2023.1108108
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