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Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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https://doi.org/10.5281/zenodo...
Dataset . 2022
License: CC BY
Data sources: Sygma
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A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation

Authors: Koppa, Akash; Rains, Dominik; Hulsman, Petra; Poyatos, Rafael; Miralles, Diego G.;

A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation

Abstract

This repository contains the datasets used in the research article "A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation". The repository contains the following files: 1) Input - contains all the processed input used for training the deep learning models and the datasets used for creating the figures in the article. 2) Output - contains the final deep learning models and the outputs (evaporation and transpiration stress factor) outputs from the hybrid model developed in the study. Formats: All scripts are in the programming language Python. The datasets are in HDF5 and NetCDF file formats. The codes related to the research article and deep learning model are available in the following repository: https://github.com/akashkoppa/StressNet

Related Organizations
Keywords

Machine Learning, Earth System Modeling, Evaporation, Evaporative Stress, Deep learning, GLEAM, Hybrid Modeling, Transpiration

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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