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Speaker

Gabriel Rodrigues Palma from Hamilton Institute, Maynooth University, Ireland.

Abstract

Insect outbreaks are among the most important biotic disturbances in forests and agroecosystems that cause economic and ecological damage. This phenomenon depends on a variety of biological and physical factors. However, given the complexity of these systems, predicting outbreaks becomes a focus of research. Recently, Hilker and Westerhoff (2007) proposed a method called Alert Zone Procedure (AZP) that is compatible with animal outbreak forecasting. This method needs to be enhanced to be able to provide better predictive power. Forecasting and classification tasks have been improved by Machine Learning algorithms that are applied in many domains of science based on the purpose of understanding how algorithms can ”learn” these tasks. For the outbreak context, in general, many methods have been developed to enhance predictions such as Recurrent Neural Networks, Random Forests, and Support Vector Machines. Here we propose the novel Pattern-Based Prediction (PBP) method for predicting population outbreaks. It is based on the Alert Zone Procedure, combined with elements from statistical machine learning.

Webinar Video

Category: Webinars