Fig 6 - uploaded by Mihail Motzev
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Cost of Forecasting Versus Cost of Inaccuracy for a Medium-Range Forecast, Given Data Availability (Source: Chambers, J., Mullick, S. and Smith, D., 1971. How to Choose the Right Forecasting Technique, Harvard Business Review, July)
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Nowadays, simulations are increasingly being used in many contexts, such as training and education in business and economics. The validity of the simulation outcomes is a key issue in simulations. Procedures and protocols for simulation model verification and validation are an ongoing field of academic study, research and development in simulations...
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... on. In real life, the two most important factors used to evaluate a simulation are its Accuracy and Cost. Other important factors include the time to gather and analyze the data, computational power and software, availability of historical data, and time horizon of the simulation. How cost and accuracy increase with sophistication is presented in Fig. 6, which shows and charts this against the corresponding cost of prediction errors, given some general assumptions. The most sophisticated technique that can be economically justified is one that falls in the region where the sum of the two costs is ...
Citations
... It presents results from international research done in Europe, Australia, and most recently in the United States. It summarizes the previous research projects discussed in Motzev (2015;2018a;2018b;2019;2021). ...
... In Motzev (2019;2021) we discussed in detail the problem of models' accuracy and how to measure it using the prediction error. The prediction error should always be calculated using actual data as a base. ...
The best model selection and the validation of the model are key issues in any model-building process. The present paper summarizes the results from international research done in Europe, Australia, and most recently in the United States. It discusses the model selection and validation in deep neural networks based on their prediction errors and provides some insights how to improve their accuracy in a very cost-effective way.
... It presents results from international research done in Europe, Australia, and most recently in the United States. It summarizes the previous research projects discussed in Motzev (2015;2018a;2018b;2019;2021). ...
... In Motzev (2019;2021) we discussed in detail the problem of models' accuracy and how to measure it using the prediction error. The prediction error should always be calculated using actual data as a base. ...
The best model selection and the validation of the model is a key issue in any model-building process. The present paper summarizes the results from international research done in Europe, Australia, and most recently in the United States. It discusses the model selection and validation in deep neural networks based on their prediction errors and provides some insights how to improve their accuracy in a very cost-effective way.
... The validation of the accuracy of the digital twin is conducted when comparing the results of the simulation under known conditions with the performance of the actual system [28]. This method yields results to within 95% accuracy of a real-time mining ventilation system, which is acceptable given the size and scale of the model [27] [29]. Figure 4 is a graphical representation of the digital twin developed for this study. ...
Deep-level mines have complex and dynamic ventilation systems to ensure that sufficient air is provided to underground workers. Certain changes to the ventilation systems are continually implemented to enable the deepening of these mines. The three main hazards in ventilation systems are high temperatures, dust pick-up, and gas build-up. This means that avoiding hazards in these systems is important for the health and safety of workers. New technologies, such as digital twinning, can be used to simulate and plan the entire deep-level mine ventilation network with ease. In this study, a digital twin is used to identify high-risk areas in a deep-level mine that are susceptible to high temperatures, dust-pick-up, and gas build-up. The identified hazards can now be avoided by implementing various changes to help mitigate the possibility of their occurrence. This enables the mining industry to plan proactively and to manage the ventilation system for the entire life-of-mine (LOM). Keywords Deep-level mine; Digital twin; Ventilation; Hazard identification; Hazard mitigation
