Creating behavioural models of human operators engaged in supervisory control tasks with UAVs is of great value due to the high cost of operator failures. Recent works in the field advocate the use of Hidden Markov Models (HMMs) and derivatives to model the operator behaviour, since they offer interpretable patterns for a domain expert and, at the same time, provide valuable predictions which can be used to detect abnormal behaviour in time. However, the first order Markov assumption in which HMMs rely, and the assumed independence between the operator actions along time, limit their modelling capabilities. In this work, we extend the study of behavioural modelling in UAV operations by using Double Chain Markov Models (DCMMs), which provide a flexible modelling framework in which two higher order Markov Chains (one hidden and one visible) are combined. This work is focused on the development of a process flow to rank and select DCMMs based on a set of evaluation measures that quantify the predictability and interpretability of the models. To evaluate and demonstrate the possibilities of this modelling strategy over the classical HMMs, the proposed process has been applied in a multi-UAV simulation environment.