Data quality and validation of radiological assessment models.
Simulation models must be tested against measurements before their predictions can be viewed as reliable. However, having data to test all pathways in a model is rare, and finding data that were collected for the purpose of model testing is even rarer. Thus modelers must take every opportunity to test portions of their models with data obtained under less than ideal circumstances. With careful preparation of the data and a thorough understanding of the data requirements of the model, the success of the test can be assured. Monitoring data collected after the Chernobyl reactor accident were used for model testing in two international studies, the Biospheric Model Validation Study and the Validation of Environmental Model Predictions. Based on these experiences, this paper describes the data requirements of validation, the validation of models with less than ideal data, the value of good data, the role of modeling in improving the quality of data and, most importantly, ways to improve collection and preparation of data to assist further validation. Although the examples are specific to radiological assessment models, the principles of data collection and preparation herein may be applied to testing all simulation models.
Peterson
S R
SR
Environmental Research Branch, AECL, Chalk River Laboratories, Ontario, Canada.
Kirchner
T B
TB
D000818:Animals; D002588:Cesium Radioisotopes; D006801:Humans; D008954:Models, Biological; D011210:Power Plants; D018788:Radioactive Hazard Release; D011848:Radioactive Pollutants; D014455:Ukraine
D002588:Cesium Radioisotopes; D011848:Radioactive Pollutants