Model validation/testing

Model Predictions vs. Field Observations The Model Validation/Testing Process... 

Dudhia, J. (1993). A nonhydrostatic version of the Penn State/NCAR mesoscale model Validation tests and simulations of an Atlantic cyclone and cold frorA Monthly Weather Rev. 121, 1493-1513. 

The basic principles of model validation, testing the residuals and the overall model, are the same as for regression analysis. The goal of this validation is to confirm that the residuals obtained are independent, normally distributed, white noise values and that the model captures a significant portion of the overall variability. The main tools for model validation are ... 

The first system identification-specific detail is that the goal of most such models is to predict future values. Therefore, the model validation tests are often performed on a separate set of data that was not used for model parameter estimation. This is one major difference from standard regression analysis where the same data set is used for both cases. This means that the data set is split into two parts one is used for model parameter estimation and one is used for model validation. In general, the model creation part will consist of A of the data, while the model validation part will consist of % of the data. 

The validity of the model is tested against the experiment. A ISOOcc canister, which is produced by UNICK Ltd. in Korea, is used for model validation experiment. In the case of adsorption, 2.4//min butane and 2.4//min N2 as a carrier gas simultaneously enter the canister and 2.1//min air flows into canister with a reverse direction during desorption. These are the same conditions as the products feasibility test of UNICK Ltd. The comparison between the simulation and experiment showed the validity of our model as in Fig. 5. The amount of fuel gas in the canister can be predicted with reasonable accuracy. Thus, the developed model is shown to be effective to simulate the behavior of adsorption/desorption of actual ORVR system. 

Cross-validation test The values of q for these QSAR models are from 0.549 to 0.972. The high values of q validate the QSAR models. From the literature, it must be greater than 0.50 [73,74]. 

Two issues present themselves when the question of PB-PK model validation is raised. The first issue is the accuracy with which the model predicts actual drug concentrations. The actual concentration-time data have most likely been used to estimate certain total parameters. Quantitative assessment, via goodness-of-fit tests, should be done to assess the accuracy of the model predictions. Too often, model acceptance is based on subjective evaluation of graphical comparisons of observed and predicted concentration values. 

Arabitol testing was performed to ensure that it behaved similarly to xylitol in this reaction to validate xylitol as a model compound. Testing was performed using Ni/Re catalyst from the initial batch screening. Shown in Table 1, the results from the two sugar alcohols were nearly equivalent. This gave some confidence that xylitol should be a valid model compound in the absence of actual hemicellulose derived feedstock. 

The goal of this paper Is to present the current status of model validation and field testing of chemical fate and transport models other papers in this symposium discuss the state-of-the-art of modeling specific processes, environments, and multimedia problems. The process of model validation, and its various components, is described considerations in field testing, where model results are compared to field observations, are discussedp an assessment of the current extent of field testing for various processes and media is presented and future field testing and data needs are enumerated. 

Figure 1 presents an overview of the model testing/valida-tion process as developed at the Pellston workshop. A distinction is drawn between validation of empirical versus theoretical models as discussed by Lassiter (4 ). In reality, many models are combinations of empiricism and theory, with empirical formulations providing process descriptions or interactions lacking a sound, well-developed theoretical basis. The importance of field data is shown in Figure 1 for each step in the model validation process considerations in comparing field data with model predictions will be discussed in a later section. 

The process of field validation and testing of models was presented at the Pellston conference as a systematic analysis of errors (6. In any model calibration, verification or validation effort, the model user is continually faced with the need to analyze and explain differences (i.e., errors, in this discussion) between observed data and model predictions. This requires assessments of the accuracy and validity of observed model input data, parameter values, system representation, and observed output data. Figure 2 schematically compares the model and the natural system with regard to inputs, outputs, and sources of error. Clearly there are possible errors associated with each of the categories noted above, i.e., input, parameters, system representation, output. Differences in each of these categories can have dramatic impacts on the conclusions of the model validation process. 

The greatest need in model performance testing and validation is clearly the use of quantitative measures to describe comparisons of observed and predicted values. As noted above, although a rigorous statistical theory for model performance assessments has yet to be developed, a variety of statistical measures has been used in various combinations and the frequency of use has been increasing in recent years. The FAT workshop (3.) identified three general types of comparisons that are often made in model performance testing ... 

Frequency domain performance has been analyzed with goodness-of-fit tests such as the Chi-square, Kolmogorov-Smirnov, and Wilcoxon Rank Sum tests. The studies by Young and Alward (14) and Hartigan et. al. (J 3) demonstrate the use of these tests for pesticide runoff and large-scale river basin modeling efforts, respectively, in conjunction with the paired-data tests. James and Burges ( 1 6 ) discuss the use of the above statistics and some additional tests in both the calibration and verification phases of model validation. They also discuss methods of data analysis for detection of errors this last topic needs additional research in order to consider uncertainties in the data which provide both the model input and the output to which model predictions are compared. 

Future needs in support of model validation and performance testing must continue to be in the area of coordinated, well-designed field data collection programs supplemented with directed research on specific topics. The FAT workshop produced a listing of the field data collection and research needs for the air, streams/lakes/estuaries, and runoff/unsaturated/saturated soil media categories, as follows ... 

In vitro tools could be used alone or in test batteries with increased potency of the description of cellular events and changes. The chapter provides a brief introduction on the components of an in vitro system, the main differences between models for research and models for testing and a list of validated alternative methods according to the European Centre for the Validation of Alternative Methods (ECVAM) (http // ecvam.jrc.it/, http //ecvam.jrc.ec.europa.eu/) evaluation. 

On the basis of different assumptions about the nature of the fluid and solid flow within each phase and between phases as well as about the extent of mixing within each phase, it is possible to develop many different mathematical models of the two phase type. Pyle (119), Rowe (120), and Grace (121) have critically reviewed models of these types. Treatment of these models is clearly beyond the scope of this text. In many cases insufficient data exist to provide critical tests of model validity. This situation is especially true of large scale reactors that are the systems of greatest interest from industry s point of view. The student should understand, however, that there is an ongoing effort to develop mathematical models of fluidized bed reactors that will be useful for design purposes. Our current... 

So validation may not only involve the time frame required to perform it, it may also involve questions of the models (or at least the number of models) being tested. 

Model validation, v - the process of testing a calibration model to determine bias between the estimates from the model and the reference method, and to test the expected agreement between estimates made with the model and the reference method. 

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