Sika deer trajectory prediction considering environmental factors by timeseries transformer-based architecture

Recently, the damage to agriculture, forestry, and fishery by wildlife has become a serious social problem. The damage caused by a conflict between wildlife and human society often causes less motivation for farming and is expected to significantly inhibit the development of local agriculture and forestry. However, the current management of wildlife is limited to primitive methods such as stabilizing the population by culling reliant on experience. To prevent this problem, the trajectory prediction of wildlife is one of the solutions for efficient wildlife management. In this study, we propose a machine learning architecture for predicting wildlife trajectories, considering surrounding environmental factors. Particularly, we propose a machine learning architecture that more accurately predicts trajectories using a timeseries transformer model that can learn long-term dependencies and add a submodel that can consider surrounding environmental factors. The proposed architecture allows interpreting which past trajectory areas should be focused on when predicting a trajectory by visualizing the weight of the focus mechanism, one of the elements comprising the transformer model. Further, the proposed architecture can decide the priority of elements of a multivariate timeseries input by considering a variable selection network (VSN), which considers the relationship of timeseries between a given time point and those before and after using casual convolutions. The main advantage of our methods is to understand the potential environmental factors to predict animal trajectories by analyzing the importance of each input feature representation in VSN. We experimented to evaluate the proposed method using one of the largest biologging datasets of sika deer ( Cervus nippon ) in Kanagawa Prefecture, Japan. The proposed method outperforms existing methods on various evaluation metrics and can effectively consider environmental factors in predicting trajectories. In addition, we analyze the results and show that the proposed architecture can pay attention to the times that individuals moved significantly within the observation period by visualizing the trajectories for the best and worst cases and the weight of each environmental factor.