What question did this study set out to answer?

To establish and validate a mathematical framework for decomposing mean squared error into distinct, interpretable components for objective model evaluation and benchmarking.

November 24, 2021Journal of Advances in Modeling Earth SystemsOpen Access

Mean Squared Error, Deconstructed

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Authors

THTimothy HodsonNorthern Illinois University TOThomas M. OverUnited States Geological Survey SFSydney S. FoksUnited States Geological Survey

Discussion

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Implication

Modeling analysis demonstrates interpretable error decomposition across 1,021 continental streamflow monitoring sites, highlighting an objective alternative to subjective composite metrics.

Key Points

To establish and validate a mathematical framework for decomposing mean squared error into distinct, interpretable components for objective model evaluation and benchmarking.
Formulated mathematical decompositions that isolate discrete error components, such as seasonality and variance, from overall mean squared error.
Evaluated the decomposition method using predictions from three streamflow models across 1,021 streamgages located throughout the conterminous United States.
Decompositions effectively partitioned aggregate error into discrete, physically meaningful concepts without requiring subjective composite error scoring.
Demonstrated that combining basic error components can systematically represent complex dynamical behaviors, including seasonal variability, across diverse hydrological monitoring stations.

Cite This Study

Hodson et al. (2021) studied this question.

synapsesocial.com/papers/69ea11ebaf6bc739daa81a6f https://doi.org/10.1029/2021ms002681

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November 24, 2021Journal of Advances in Modeling Earth SystemsOpen Access

Mean Squared Error, Deconstructed

View Full Paper

Ask AI

Bookmark

Authors

THTimothy HodsonNorthern Illinois University TOThomas M. OverUnited States Geological Survey SFSydney S. FoksUnited States Geological Survey

Discussion

Member takes

Implication

Modeling analysis demonstrates interpretable error decomposition across 1,021 continental streamflow monitoring sites, highlighting an objective alternative to subjective composite metrics.

Key Points

To establish and validate a mathematical framework for decomposing mean squared error into distinct, interpretable components for objective model evaluation and benchmarking.
Formulated mathematical decompositions that isolate discrete error components, such as seasonality and variance, from overall mean squared error.
Evaluated the decomposition method using predictions from three streamflow models across 1,021 streamgages located throughout the conterminous United States.
Decompositions effectively partitioned aggregate error into discrete, physically meaningful concepts without requiring subjective composite error scoring.
Demonstrated that combining basic error components can systematically represent complex dynamical behaviors, including seasonal variability, across diverse hydrological monitoring stations.

Cite This Study

Hodson et al. (2021) studied this question.

synapsesocial.com/papers/69ea11ebaf6bc739daa81a6f https://doi.org/10.1029/2021ms002681

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