# Animal Movement Prediction Based on Predictive Recurrent Neural Network
## 1 Introduction  
Analyzing animal movements is the first step toward understanding the ecosystem. Animal movements provide potential information for obtaining ecological insights on topics such as habitat selection, population dynamics, and group behavior. Over the decades, many studies have been carried out based on the awareness of this importance [1; 2; 3; 4]. Since the 1990s, the development of telemetry technologies such as the global positioning system (GPS) and advanced research and global observation satellite (ARGOS) has accelerated various studies to model animal movements. Furthermore, with the availability of remote sensing technology, diverse meteorological and geographical sensing data can be continuously acquired, and the amount is sufficient to carry out reasonable modeling. Many scientists have emphasized the need to use modeling processes to understand animal movements and the factors correlated with such movements [5; 6; 7; 8]. Recently, as the versatility of machine learning methodologies has been observed in various domains, many efforts have been made to model animal movements using machine learning [9; 10; 11; 12]. For instance, [PERSON] et al. proposed a two-phase prediction scheme to classify two animal behavior states: transient and resident. They used the maximum likelihood and monte carlo (MC) methods, and a hidden markov model (HMM) was used to predict the potential state of behavior change [13]. MoveHMM [14], which is a representative modeling tool based on hidden markov processes, showed that probability-based prediction of state switching is possible by using features such as animal steplength and turning angle. More recently, the deep neural network (DNN) has become a major tool in animal movement modeling. For example, [PERSON] et al. focused on filling in the observation gap that frequently occurred while tracking wild animals periodically [15]. They found that previous interpolation methods (e.g., linear, curvilinear, random walk) are not sufficient to recover large spatial gaps. For this reason, they used inverse reinforcement learning (IRL) to recover the trajectory by estimating the gap as a reward function. [PERSON] et al. utilized a six-layer convolution neural network (CNN) to distinguish fish shapes and track the zebrafish trajectory [16]. [PERSON] et al. proposed a deep architecture to predict the diving behavior of seabirds associated with foraging. They combined the GPS and time depth record (TDR) from 108 individuals and trained deep learning models for predicting the behavior of European shags, common guillemots, and razorbills. An optimized model achieved 94% and 80% prediction accuracy of non-diving and diving behavior, respectively [17]. Despite the great effort to build animal movement models using geo records (e.g., GPS, ARGOS), the results have been limited to the identification of animal movement state or the analysis of correlations with environmental conditions. The key function that should be implemented in such a model is the prediction of animal movements according to changes in various spatiotemporal factors, which is possible by considering historical movement patterns. Such a model could be used for a variety of purposes, such as understanding animal behavior or protecting animal habitats, but to the best of our knowledge, there are very few reports on this topic. Therefore, in this paper, we propose an animal movement prediction scheme based on predictive recurrent neural networks (RNNs). To improve the prediction accuracy, we focus on three issues, which are our main contributions. (1) To fill the observation gap, we propose an optimized random forest-based interpolation scheme that considers weather information and movement-related vectors. In fact, interpolating the animal pathway is a very important task for predicting animal movement when considering realistic conditions. (2) We propose a method to construct an image sequence of representative animal movements by using the kernel density estimation (KDE) algorithm.