Application of air pollution dispersion modeling for source-contribution assessment and model performance evaluation at integrated industrial estate-Pantnagar

The tunnel module of a rather simple Lagrangian model GRAL (Grazer Langrange model) has been chosen to study air pollutant dispersion around tunnel portals in Nanjing inner ring. Two points have been made to popularize GRAL3.5TM (the tunnel module of a Lagrangian model GRAL; the update was in May 2003) and assure it more suitable for the actual situations in Nanjing. One is to derive a piecewise function of the intermediate parameter ‘stiffness’. Another is to take Romberg NOx-NO2 scheme into account. After these 2 works on GRAL3.5TM, NO2 dispersion from portals of all the 6 tunnels in Nanjing inner ring has been simulated. The importance of limiting urban traffic volume to control air quality around tunnel portals and roadways has been emphasized.

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A Transport and Dispersion Model Performance Evaluation Using the Results of a Tracer Experiment in Complex Terrain

Air Pollution Modeling and Its Application X ◽

10.1007/978-1-4615-1817-4_45 ◽

1994 ◽

pp. 413-421

Author(s):

R. Lamprecht ◽

D. Berlowitz

Keyword(s):

Performance Evaluation ◽

Complex Terrain ◽

Dispersion Model ◽

Model Performance ◽

Tracer Experiment ◽

Model Performance Evaluation

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

Advances in Methods and Practices in Psychological Science ◽

10.1177/25152459211026864 ◽

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.

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