scholarly journals On the criteria of model performance evaluation for real-time flood forecasting

2016 ◽  
Vol 31 (5) ◽  
pp. 1123-1146 ◽  
Author(s):  
Ke-Sheng Cheng ◽  
Yi-Ting Lien ◽  
Yii-Chen Wu ◽  
Yuan-Fong Su
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Model Performance ◽  
Flood Forecasting ◽  
Model Performance Evaluation

scholarly journals Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction

2021 ◽  
Vol 4 (3) ◽  
pp. 251524592110268
Author(s):  
Roberta Rocca ◽  
Tal Yarkoni
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Model Evaluation ◽  
Evaluation Criteria ◽  
Model Performance ◽  
Scientific Models ◽  
Practical Utility ◽  
Model Performance Evaluation ◽  
Unseen Data ◽  
Reliable Model

Consensus on standards for evaluating models and theories is an integral part of every science. Nonetheless, in psychology, relatively little focus has been placed on defining reliable communal metrics to assess model performance. Evaluation practices are often idiosyncratic and are affected by a number of shortcomings (e.g., failure to assess models’ ability to generalize to unseen data) that make it difficult to discriminate between good and bad models. Drawing inspiration from fields such as machine learning and statistical genetics, we argue in favor of introducing common benchmarks as a means of overcoming the lack of reliable model evaluation criteria currently observed in psychology. We discuss a number of principles benchmarks should satisfy to achieve maximal utility, identify concrete steps the community could take to promote the development of such benchmarks, and address a number of potential pitfalls and concerns that may arise in the course of implementation. We argue that reaching consensus on common evaluation benchmarks will foster cumulative progress in psychology and encourage researchers to place heavier emphasis on the practical utility of scientific models.

Air pollution modeling for an industrial complex and model performance evaluation

2001 ◽  
Vol 111 (3) ◽  
pp. 471-477 ◽  
Author(s):  
R Sivacoumar ◽  
A.D Bhanarkar ◽  
S.K Goyal ◽  
S.K Gadkari ◽  
A.L Aggarwal
Keyword(s):  
Air Pollution ◽  
Performance Evaluation ◽  
Model Performance ◽  
Industrial Complex ◽  
Air Pollution Modeling ◽  
Model Performance Evaluation ◽  
Pollution Modeling

Resampling and extreme value statistics in air quality model performance evaluation

1985 ◽  
Vol 19 (9) ◽  
pp. 1503-1518 ◽  
Author(s):  
S.T. Rao ◽  
G. Sistla ◽  
V. Pagnotti ◽  
W.B. Petersen ◽  
J.S. Irwin ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Air Quality ◽  
Model Performance ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Air Quality Model ◽  
Quality Model ◽  
Model Performance Evaluation

Model Performance Evaluation and Bias Correction Effect Analysis for Forecasting PM2.5 Concentrations

2017 ◽  
Vol 33 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Young Sung Ghim ◽  
Yongjoo Choi ◽  
Soontae Kim ◽  
Chang Han Bae ◽  
Jinsoo Park ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Bias Correction ◽  
Model Performance ◽  
Effect Analysis ◽  
Model Performance Evaluation ◽  
Correction Effect

scholarly journals Extensions to the Visual Predictive Check to facilitate model performance evaluation

2008 ◽  
Vol 35 (2) ◽  
pp. 185-202 ◽  
Author(s):  
Teun M. Post ◽  
Jan I. Freijer ◽  
Bart A. Ploeger ◽  
Meindert Danhof
Keyword(s):  
Performance Evaluation ◽  
Visual Predictive Check ◽  
Model Performance ◽  
Model Performance Evaluation

COMPLEX I and II Model Performance Evaluation in Nevada and New Mexico

1983 ◽  
Vol 33 (9) ◽  
pp. 864-871 ◽  
Author(s):  
Paul D. Gutfreund ◽  
Chung S. Liu ◽  
Bruce R. Nicholson ◽  
Edwin M. Roberts
Keyword(s):  
Performance Evaluation ◽  
New Mexico ◽  
Complex I ◽  
Model Performance ◽  
Model Performance Evaluation
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