Predicting pest outbreaks: comparing machine learning methods and alert zone procedure.

• Grant number: 20/06147-7
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: November 01, 2020
• End date: October 31, 2021
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Wesley Augusto Conde Godoy
• Grantee: Gabriel Rodrigues Palma
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Company: Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz (ESALQ)
• Associated research grant: 18/02317-5 - Center for Excellence in Biological Control (Fapesp-Koppert), AP.PCPE

Abstract

In time series of insect populations, it is generally common to observe peaks and dipsin population size, which can often become recurrent. This is sometimes character-ized by a wide spectrum, resulting in abrupt population outbreaks for several reasons.For these cases, it is particularly important to carrying out analyses to project the riskof occurrence of these population levels, in order to apply measures to prevent unde-sirable events. The periods prior to outbreaks can be characterized as "Alert Zones"which may contain recurrent ecological patterns useful for predictions. The detectionof zones can result in population diagnostics important for decision making in highly-oscillated systems that commonly occur in insects, especially pests. A few years ago,a new way for carrying out pattern analysis, the Alert Zone Procedure (AZP), wasproposed. The essence of this ideia is absolutely compatible with insect outbreakforecasting, but to do that new improvements have to be completed allowing for a bet-ter predictive power. Currently, the tasks of forecasting and classification have beenimproved by the new Machine Learning (ML) algorithms that are applied in many do-mains of science based on the purpose of understanding how algorithms can "learn"these tasks. For the outbreak context, in general, many methods have been developedto enhance predictions such as Recurrent Neural Networks, Random Forests, Sup-port Vector Machines and others. The results in terms of prediction of these methodsseem to be very promising to aid in predicting pest outbreaks. Therefore, the contextof the current project is based on pest management, Machine Learning and statis-tics. This interdisciplinary project has two main goals: (1) to improve the AZP and (2)to compare the pest outbreak predictive power of the improved AZP and different MLmethods.

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

PALMA, GABRIEL R.; GODOY, WESLEY A. C.; ENGEL, EDUARDO; LAU, DOUGLAS; GALVAN, EDGAR; MASON, OLIVER; MARKHAM, CHARLES; MORAL, RAFAEL A.. Pattern-based prediction of population outbreaks. ECOLOGICAL INFORMATICS, v. 77, p. 10-pg., 2023-11-01. (19/14805-7, 20/06147-7)