Image-based machine learning for reduction of user fatigue in an interactive model calibration system

The interactive multiobjective genetic algorithm (IMOGA) is a promising new approach to calibrate models. The IMOGA combines traditional optimization with an interactive framework, thus allowing both quantitative calibration criteria as well as the subjective knowledge of experts to drive the search for model parameters. One of the major challenges in using such interactive systems is the burden they impose on the experts that interact with the system. This paper proposes the use of a novel image-based machine-learning (IBML) approach to reduce the number of user interactions required to identify promising calibration solutions involving spatially distributed parameter fields (e.g., hydraulic conductivity parameters in a groundwater model). The first step in the IBML approach involves selecting a few highly representative solutions for expert ranking. The selection is performed using unsupervised clustering approaches from the field of image processing, which group potential parameter fields based on their spatial similarities. The expert then ranks these representative solutions, after which a machine-learning model (augmented with the spatial information of the selected fields) is trained to learn user preferences and predict rankings for solutions not ranked by the expert. To better mimic the "visual" information processing of human experts, algorithms from the field of image processing are used to mine information about the spatial characteristics of parameter fields, thus improving the performance of the clustering and machine-learning algorithms. The IBML approach is tested and demonstrated on a groundwater calibration problem and is shown to lead to significant improvements, reducing the amount of user interaction by as much as half without compromising the solution quality of the IMOGA.