A new novel index for evaluating model performance
By M. Ali, I. Abustan. 2014
73 citations recorded by the source corpus.
Received 27.01.2013 Accepted 11.06.2013 Published 27.01.2014 A vast array of scientific literature is concerned with simulation models. The aim of models is to predict the unknown situation as close to as real one. To do this, models are validated and examined for their performance under known condition. In this paper, commonly used model performance evaluation indices are overviewed and examined under different situations. Difference based, efficiency based (Nash and Sutcliffe coefficient, model efficiency of Loague and Green, Legates and McCabe’s index) and composite indices (such as index of agreement, d, and dr) were found ambiguous, inconsistent and not logical in many cases. A new index, Percent Mean Relative Absolute Error (PMRAE), is proposed which is found unambiguous, logical, straight-forward, and interpretable; thus can be used to evaluate model performance. The model evaluation performance ratings based on PMRAE are also suggested. Model evaluation Statistical indicators Model performance Journal of Natural Resources and Development 2014; 04: 1-9 1
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/d9ddca7b6ff2f363c86a3957a0847d066602994d
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In Artificial Intelligence
Citations in the corpus
- Comparison of dual crop coefficient method and Shuttleworth–Wallace model in evapotranspiration partitioning in a vineyard of northwest China (2015, methodology): “7e and f), with RMSE, MAE and PMARE of 0.36, 0.27 mm d−1 and 55.4%, but 0.81, 0.62 mm d−1 and 104.8% for S–”