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Open AccessJournal ArticleDOI

Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation

David R. Legates, +1 more
- 01 Jan 1999 - 
- Vol. 35, Iss: 1, pp 233-241
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TLDR
In this paper, the goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed.
Abstract
Correlation and correlation-based measures (e.g., the coefficient of determination) have been widely used to evaluate the “goodness-of-fit” of hydrologic and hydroclimatic models. These measures are oversensitive to extreme values (outliers) and are insensitive to additive and proportional differences between model predictions and observations. Because of these limitations, correlation-based measures can indicate that a model is a good predictor, even when it is not. In this paper, useful alternative goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed. Modifications to these statistics to aid in interpretation are presented. It is concluded that correlation and correlation-based measures should not be used to assess the goodness-of-fit of a hydrologic or hydroclimatic model and that additional evaluation measures (such as summary statistics and absolute error measures) should supplement model evaluation tools.

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Citations
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Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts

TL;DR: In this article, the authors proposed a hybrid CEEMD-RF-KRR model for forecasting rainfall at Gilgit, Muzaffarabad, and Parachinar in Pakistan at monthly time scale.
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Hybrid Wavelet-Genetic Programming Approach to Optimize ANN Modeling of Rainfall-Runoff Process

TL;DR: The obtained results showed that the proposed model can monitor both short and long term patterns due to the use of multiscale time series of rainfall and runoff data as the GP inputs, and was compared favorably to ANN and GP models.
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Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds

TL;DR: The performance of feed forward CNNs to forecast one-day ahead daily flows at large Portuguese watersheds considering that only flows in previous days are available for the calibration of the models were analyzed and showed that it is also possible to get daily flows forecasts at watersheds with insufficient flow data.
Journal ArticleDOI

Evaluating Goodness-of-Fit Measures for Synthetic Microdata

TL;DR: Goodness-of-fit tests are widely used by geographers as mentioned in this paper, but choice remains difficult, and three important approaches to assessing the goodness of categorical data are reviewed and appraised: statistics tested against the h ² distribution, the normal Z score, and measures derived from information theory.

Modeling Inter-annual Variability of Seasonal Evaporation and Storage Change Based on the Extended Budyko Framework

TL;DR: In this paper, a modified Turc-Pike equation with a horizontal shift is proposed to model interannual variability of seasonal evaporation ratio as a function of seasonal aridity index, which includes rainfall seasonality and soil water change.
References
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Journal ArticleDOI

River flow forecasting through conceptual models part I — A discussion of principles☆

TL;DR: In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.
Journal ArticleDOI

On the validation of models

TL;DR: In this paper, it is suggested that the correlation coefficieness between observed and simulated variates is not as good as observed variates, and that correlation can be improved.
Journal ArticleDOI

A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation

TL;DR: This paper reviewed the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule, at a relaxed mathematical level, omitting most proofs, regularity conditions and technical details.
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