Computational models calibration. Experiences in environmental studies

Abstract

Mathematical models are a fundamental tool in the learning process of environmental engineering. These models need to be calibrated in order to be used by future engineers as a simulation tool for the represented problems. This paper deals with the concept of computational models calibration applied to higher environmental engineering studies. In this paper, we depict a methodology to calibrate water quality models, as an educational example that represents the environmental problem of dissolved oxygen in a stream. This methodology is based on defining two types of parameters involved in calibration. First, internal parameters appear in the equations from semi-empirical estimations and can be found within some intervals. Genetic algorithms are proposed to estimate them. Second, experimental measurements enter into equations as external parameters. They affect the accuracy of the final solutions. Therefore, an uncertainty analysis has to be performed. Finally, a termination criterion for calibration has been proposed, based on the overlap between the confidence intervals of predicted and measured values. By developing this methodology, we provide awareness to our students of the importance of calibration of mathematical models so that they can apply them in their future simulation of environmental problems. Students identify the possible sources of uncertainty at each stage of the environmental model performance and apply them in this particular problem, Genetic Algorithm Techniques, as a computational tool to improve the accuracy of their model predictions