Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models

Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance g...

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Bibliographic Details
Main Authors: Zaherpour, Jamal, Mount, Nick J., Gosling, Simon N., Dankers, Rutger, Eisner, Stephanie, Dieter, Gerten, Liu, Xingcai, Masaki, Yoshimitsu, Müller Schmied, Hannes, Tang, Qiuhong, Wada, Yoshihide
Format: Article
Language:English
Published: Elsevier 2019
Subjects:
Machine Learning; Model Weighting; Gene Expression Programming; Global Hydrological Models; Optimization
Online Access:https://eprints.nottingham.ac.uk/55925/

Internet

https://eprints.nottingham.ac.uk/55925/

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