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Pre-trained models for "A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling"

Authors: Kratzert, Frederik; Klotz, Daniel; Hochreiter, Sepp; Nearing, Grey;

Pre-trained models for "A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling"

Abstract

This dataset contains the pre-trained models from the publication "A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling". For each input configuration, the dataset contains 10 model repetitions. Each run has a separate folder, containing the model weights, run configuration, validation and test set results. The models were trained using the code available at https://github.com/kratzert/multiple_forcing Paper reference (accepted for publication): Kratzert, F., Klotz, D., Hochreiter, S., and Nearing, G. S.: A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-221, in review, 2020. The paper is available at https://doi.org/10.5194/hess-2020-221

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Keywords

Deep Learning, Hydrology, LSTM

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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.
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