Enhancing Anti-Poaching Efforts Through Predictive Analysis Of Animal Movements And Dynamic Environmental Factors

This degree project addresses poaching challenges by employing predictive analysis of animal movements and their correlation with the dynamic environment using a machine learning approach. The goal is to provide accurate predictions of animal movements, enabling rangers to intercept potential threats and safeguard wildlife from snares. A wide analysis considers previous studies on animal movements and both animal and environment data availability. To efficiently represent the dynamic environment and correlate it with animal movement data, accurate matching of environment variables to each animal measurement is crucial. We selected multiple environment datasets to capture a sufficient amount ofenvironmental properties. Due to practical constraints, daily representation of the environment is not achievable, and weekly mean or monthly mode values are used instead. Data insights are obtained through the training of a regression neural network using the filtered environmental and animal movement data. The results highlight the significant role ofenvironmental features in predicting animal movements, emphasizing their importance for accurate predictions. Despite some offset and few erroneous predictions, a strong similarity between animal predicted trajectory and animal true trajectory was achieved, indicating that the model is capable to capture general patterns and to correctly tune in predictions of detailed movements as well. The overall offset of the trajectories is still a weak point of this model, but it may just indicate the presence of some underlying systematic error that can be corrected through further work. The integration of such a developed prediction model into existing frameworks could assist law enforcingauthorities in preventing poaching activities