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Engineers are often faced with attempting to characterize data from an experiment without having complete knowledge of the experiment itself or the complete physics of the problem. There may also be limited access to data due to proprietary and/or classification issues. In such cases, developing a physics-based, first-principle model may not be pos...
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Figure 2 demonstrates visually that the simulated output — in blue — compares favorably with the original observation — in green. This qualitative comparison must also be quantified to be of value in forecasting. The purely forecasted values lay from samples 1200 to 1500. Despite the favorable visual comparison, a quantitative measure of the goodness of fit is desirable to validate this method. The Captain Toolbox authors use a version of the Coefficient of Determination, , to compare simulations with observed data for time series data. This measure is defined by: In the definition above, y is the observation data set and fit is the simulated data [Captain Toolbox Handbook]. When tf , the simulation from the previous example is compared with the observed data, y , the result is also ...
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Given recent circular economy policy and waste minimisation targets, there is a significant opportunity to fundamentally change the way waste is managed in Australia, and re-focus waste management to promote resource recovery and efficiency. Detailed data on household waste generation can assist decision makers in targeting waste minimisation incentives, improving resource recovery and circularity, identifying specific technology and infrastructure gaps and informing future development. Unfortunately, high-resolution spatial estimates of waste generation at the property lot scale is typically unavailable. This study presents a novel spatial model developed to estimate waste generation data at the property lot level. Utilising census data at multiple spatial scales and council waste generation data, we apply our model to estimate quantities of residual waste, dry recyclables and garden waste generated for more than 1,200,000 property lots in the Sydney metropolitan area, Australia. Results show the spatial distribution of estimated household waste generation, achieving a high degree of accuracy when compared to validation data. To illustrate the application of our results in the context of identifying ideal areas for waste processing facilities, we analyse the spatial distribution of available garden waste arising from property lots. An area of intense garden waste generation was identified, indicating a supply area of approximately 13km2 in northern Sydney that can support a facility of approximately 20,000t throughput a year. Our analytical approach presented is novel, and has practical application for locating waste processing facilities; analysing efficient kerbside waste collection services; and in informing data driven urban waste management strategies.
