Models are simplified representations of real-world processes, and they have proved to be very effective tools for planning and decision-making. There are two model types. Mechanistic models implement the physical equations of real-world processes to determine the relationships between inputs and outputs. Data-driven models use statistical techniques to determine those same relationships. Hybrids are a combination of both types of models.
Which type of model is most effective depends on its usage. With well-known physical processes, mechanistic models generally excel. Hydroquo+ experts, from hydraulic engineers to chemical engineers, effect mechanistic approaches via software packages that first build the model, then iteratively calibrate and test it until the model behaviour is close to the mimicked system. In contrast, data-driven models can be used with more complex systems where there are no well-defined relationships between inputs and outputs but there is a large amount of historical data available on past events within that system.
Which type of model is most effective depends on its usage. With well-known physical processes, mechanistic models generally excel. Hydroquo+ experts, from hydraulic engineers to chemical engineers, effect mechanistic approaches via software packages that first build the model, then iteratively calibrate and test it until the model behaviour is close to the mimicked system. In contrast, data-driven models can be used with more complex systems where there are no well-defined relationships between inputs and outputs but there is a large amount of historical data available on past events within that system.
Data-driven models use statistical techniques such as linear regression to find correlations between variables in the historical data set. These correlations are then used as proxies for causal relationships between inputs and outputs in predicting future outcomes from changes in Anticipate, predict and trigger alerts with lead-in times
After developing your model, it is necessary to test it using real-life data. If you have enough data available for testing, then you can use statistical methods for evaluating whether or not your model accurately predicts future outcomes. However, if you do not have enough data for testing purposes then there are other methods available for estimating accuracy levels such as Monte Carlo simulations or sensitivity analysis (see below). Once testing has been completed successfully then we can move on to using our model in practice!
