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Performance of statistical and machine learning ensembles for daily temperature downscaling

https://doi.org/10.1007/s00704-020-03098-3
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Outgoing claims: the papers this work cites, builds on, or argues with.

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inferred← citeshttps://doi.org/10.3390/w12071885openalex-snapshot
Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study
inferred← citeshttps://doi.org/10.3390/w14213494openalex-snapshot
A Continuous Multisite Multivariate Generator for Daily Temperature Conditioned by Precipitation Occurrence
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Machine learning-based downscaling of aerosol size distributions from a global climate model
inferred← citeshttps://doi.org/10.5194/gmd-2020-214-rc2openalex-snapshot
RC2: Review of "Deep-learning based climate downscaling using the super-resolution method: a case study over the western US"
inferred← citeshttps://doi.org/10.5194/gmd-2023-158openalex-snapshot
Deep-learning statistical downscaling of precipitation in the middle reaches of the Yellow River: A Residual in Residual Dense Block based network
inferred← citeshttps://doi.org/10.5194/gmd-2023-158-ac1openalex-snapshot
Reply on CEC1
inferred← citeshttps://doi.org/10.5194/gmd-2023-158-ac2openalex-snapshot
Reply on RC1
inferred← citeshttps://doi.org/10.5194/gmd-2023-158-ac3openalex-snapshot
Reply on RC2
inferred← citeshttps://doi.org/10.5194/gmd-2023-158-cec1openalex-snapshot
Comment on gmd-2023-158
inferred← citeshttps://doi.org/10.5194/gmd-2023-158-ec1openalex-snapshot
Comment on gmd-2023-158
inferred← citeshttps://doi.org/10.5194/gmd-2023-158-rc1openalex-snapshot
Comment on gmd-2023-158
inferred← citeshttps://doi.org/10.5194/gmd-2023-158-rc2openalex-snapshot
Comment on gmd-2023-158
inferred← citeshttps://doi.org/10.71107/j2djza91openalex-snapshot
Improved Temperature Trend Forecasting in Major Pakistani Cities Using XGBoost and other Ensemble Machine Learning Models
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.