Evaluation of Machine Learning Models for Species Distribution Modeling in the Amazon

Abstract

Species Distribution Modelling (SDM) is widely used by ecologists to monitor biodiversity and manage wildlife. In the last decades, Artificial Intelligence (AI) and Machine Learning (ML) techniques became popular and were successfully applied for different tasks, including SDM. The objective of this article was to evaluate Machine Learning models for Species Distribution Modeling in the Amazon Basin region near Manaus (AM), based on meteorological and aerosol data collected by the GoAmazon 2014/15 project. The techniques were evaluated regarding their accuracy and the Decision Tree Classifier and the Maximum Entropy Model obtained good predictive performances.