Model Evaluation Sample Clauses
The Model Evaluation clause defines the procedures and criteria for assessing the performance and suitability of a model, typically in the context of machine learning or artificial intelligence projects. It outlines how the model will be tested, what metrics or benchmarks will be used, and who is responsible for conducting the evaluation. For example, it may specify that the model must achieve a certain accuracy on a validation dataset before deployment. This clause ensures that both parties have a clear understanding of the standards the model must meet, thereby reducing disputes and ensuring the model fulfills its intended purpose.
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Model Evaluation. The evaluation of the model was done by comparing the measured data with the simulated temporal variations of Ta, RH, WS, Tmrt and the physiological equivalent temperature index (PET) for thermal comfort assessement. Initially, the analysis involved calculation of mean values for the whole period the mobile sensor was measuring in each site. However, results for Tmrt were affected by the adaptation time required by the sensor when changing from one site to another. This was noticed specially when moving from sunshine to shadow sites and vice versa. With the aim of having representative data, 10 minute mean values were calculated for the data collected in each site (▇▇▇▇▇▇▇▇ et al., 2007). During the first 10 minutes, mean Tmrt was calculated with the last minute value of the period considering the measurement at this moment was already reliable. For the following 10 minutes at the same site, mean Tmrt was calculated with the 1-minute Tmrt values. In the case of Ta, RH, WS, 10-minute mean values were always calculated with 1 minute values. Tmrt values were derived from Tg measurements following the equation (▇▇▇▇▇▇▇▇ et al., 2007): To evaluate model performance, at each site every 10-minute mean measurement of Ta, RH, WS and Tmrt was compared with the temporally closest modelled data (i.e. every 30 minutes). Comparison was made with ENVI-met output values at 1 meter height. Thermal comfort was determined using the PET index (▇▇▇▇▇, 1999). It assesses thermal comfort (Table 15) by taking into account Ta and RH conditions as well as radiation and wind data (i.e. Tmrt and WS). Additionally, the human metabolic heat rate and other personal parameters need to be considered (e.g. age, gender, clothing, weight and height). For this study, PET index was calculated with standardized data (age: 35 years, height: 1.75; metabolic rate: 80 W/m2; clothing: 0.9; weight: 75 kg; sex: man). To analyze the most relevant variables that explain the differences between measured and modelled data, regression analyses (stepwise method) was used. In this case, the PET differences (measured-modelled) were considered as the dependent variable while the independent variables were the differences in Tmrt, WS, T and RH. In the stepwise regression, variables can be entered or removed depending on the model significance (probability) of the F-value or the eigenvalue of F. Seven regression analyses have been conducted. The first one included all data available (i.e. 338 pairs of measured a...
Model Evaluation. This allows the data scientist to decide if the performance of the model is good enough (proceed to the deployment step) or not (back to step 5 or 3).
Model Evaluation. Using the updated, verified model, SEH will examine the distribution system adequacy and recommend improvements. The model will simulate the operation of the ▇▇▇▇▇ Park water system during average day, maximum day, and fire flow events. Water system operational flow capacities and system pressures will be examined to assure that the water system can deliver an effective level of service. System resiliency will be tested to determine if redundant water lines are needed. In Task 2, historical water system demands will be analyzed to determine average per capita water use and peak water system demands. SEH will pair this data with future land use and population projections to estimate water system demands through 2040. As part of Task 3, these future demands will be compared with existing water supply, treatment, distribution, and storage capacities. If water system deficiencies are identified as part of this effort, SEH will provide alternatives for water system improvements. Key components of Task 3 will include: • Updating the City’s existing water model using current GIS sources for water infrastructure • Associating demands to water users (model junction nodes) spatially throughout the water system, assigning each demand to the correct location. GIS geocoding will be used to locate meters based on addresses from billing records; a demand allocator tool will automatically assign demands based on the GIS fields. • Allocating update water demands throughout the entire water system. • Developing diurnal curves for the full water system representing maximum day conditions. • Creating hydraulic profile drawings of the complete water system. • Conducting operational data review to complete an extended period calibration of system facilities. SEH will request data for one (1) historical maximum demand week including system demand, tank levels, and pumping flow rates on an hourly basis (minimum). • Performing an evaluation of the full distribution system using the updated and calibrated water model. − 10-day extended period simulations will be used for scenario evaluations. − Time periods for consideration will include the current system (2020 data), 10-year (2030), and 20-year (2040). • Developing a hydraulic analysis plan to include the following: − System configuration and pressure management. − Water supply capacity analysis. − Storage volume capacity analysis. − Fire flow capacity analysis. − Emergency operations analysis. − Scenarios required to analyze the...
Model Evaluation. The numerical accuracy of the UTCHEM model has been evaluated through a series of tests including comparisons with analytical solutions and experimental data. The numerical accuracy of UTCHEM model was evaluated by comparison with analytical solutions for problems such as the 1-D water tracer, 2-D tracer, and polymerflood examples given in Fig. 1.3 (▇▇▇ et al., 1994) and by comparison with 2-D laboratory column data of ▇▇▇▇▇▇▇ et al. [1996]. The experiment involved a 2-D horizontal sandpack contaminated with tetrachloroethylene (PCE). A mixture of surfactant solution was injected under both mobilization and solubilization conditions for PCE removal from the column. The UTCHEM model with the recently added trapping number (▇▇▇, 1995; ▇▇▇▇▇▇▇ et al., 1996) was used to model this experiment. The column was packed with 40-270 mesh Ottawa sand with a permeability of 16.3 darcies and porosity of 0.3509. Table 1.2 gives the physical properties. The surfactant solution was a 4% 1:1 mixture of sodium dihexyl sulfosuccinate and sodium dioctyl sulfosuccinate (Aerosol AY/OT) in 500 mg/L CaCl2. The measured phase behavior and fluid properties such as viscosity, density, and desaturation data were used to obtain the UTCHEM input parameters. The injection rate was at 4.95 cc/min (0.0488 ft3/day). ▇▇▇▇▇▇▇ et al.
Model Evaluation