RT info:eu-repo/semantics/article
T1 Machine learning classification–Regression schemes for desert locust presence prediction in western Africa
A1 Cornejo Bueno, Laura
A1 Pérez Aracil, Jorge
A1 Casanova Mateo, Carlos
A1 Sanz Justo, María Julia
A1 Salcedo Sanz, Sancho
K1 Desert locust
K1 Plagas de insectos
K1 Langosta (Insecto)
K1 Classification
K1 Regression analysis
K1 Análisis de regresión
K1 Statistical Learning
K1 Machine learning
K1 Aprendizaje automático
K1 Artificial intelligence
K1 Remote sensing
K1 Teledeteccion
K1 1203.04 Inteligencia Artificial
K1 1209.01 Estadística Analítica
AB For decades, humans have been confronted with numerous pest species, with the desert locust being one of the most damaging and having the greatest socio-economic impact. Trying to predict the occurrence of such pests is often complicated by the small number of records and observations in databases. This paper proposes a methodology based on a combination of classification and regression techniques to address not only the problem of locust sightings prediction, but also the number of locust individuals that may be expected. For this purpose, we apply different machine learning (ML) and related techniques, such as linear regression, Support Vector Machines, decision trees, random forests and neural networks. The considered ML algorithms are evaluated in three different scenarios in Western Africa, mainly Mauritania, and for the elaboration of the forecasting process, a number of meteorological variables obtained from the ERA5 reanalysis data are used as input variables for the classification–regression machines. The results obtained show good performance in terms of classification (appearance or not of desert locust), and acceptable regression results in terms of predicting the number of locusts, a harder problem due to the small number of samples available. We observed that the RF algorithm exhibited exceptional performance in the classification task (presence/absence) and achieved noteworthy results in regression (number of sightings), being the most effective machine learning algorithm among those used. It achieved classification results, in terms of F-score, around the value of 0.9 for the proposed Scenario 1.
PB MDPI
SN 2076-3417
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/68422
UL https://uvadoc.uva.es/handle/10324/68422
LA eng
NO Applied Sciences, 2023, Vol. 13, Nº. 14, 8266
NO Producción Científica
DS UVaDOC
RD 31-jul-2024