Forecasting insect abundance using time series embedding and machine learning
Implementing insect monitoring systems provides an excellent opportunity to create accurate interventions for insect control. However, selecting the appropriate time for an intervention is still an open question due to the inherent difficulty of implementing on-site monitoring in real-time. A possib...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S157495412400476X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595423162793984 |
|---|---|
| author | Gabriel R. Palma Rodrigo F. Mello Wesley A.C. Godoy Eduardo Engel Douglas Lau Charles Markham Rafael A. Moral |
| author_facet | Gabriel R. Palma Rodrigo F. Mello Wesley A.C. Godoy Eduardo Engel Douglas Lau Charles Markham Rafael A. Moral |
| author_sort | Gabriel R. Palma |
| collection | DOAJ |
| description | Implementing insect monitoring systems provides an excellent opportunity to create accurate interventions for insect control. However, selecting the appropriate time for an intervention is still an open question due to the inherent difficulty of implementing on-site monitoring in real-time. A possible solution to enhance decision-making is to apply forecasting methods to predict insect abundance. However, another layer of complexity is added when other covariates are considered in the forecasting, such as climate time series collected along the monitoring system. Multiple combinations of climate time series and their lags can be used to build a forecasting method. Therefore, we propose a new approach to address this problem by combining statistics, machine learning, and time series embedding. We used two datasets containing a time series of aphids and climate data collected weekly in two municipalities in Southern Brazil for eight years. We conduct a simulation study based on a probabilistic autoregressive model with exogenous time series based on Poisson and negative binomial distributions to evaluate the performance of our approach. We pre-processed the data using our newly proposed approach and more straightforward approaches commonly used to train learning algorithms. We evaluate the performance of the selected algorithms by looking at the Pearson correlation and Root Mean Squared Error obtained using one-step-ahead forecasting. Based on Random Forests, Lasso-regularised linear regression, and LightGBM regression algorithms, we showed the feasibility of our novel approach, which yields competitive forecasts while automatically selecting insect abundances, climate time series and their lags to aid forecasting. |
| format | Article |
| id | doaj-art-c4090bef380a4eca8fe4f3ee9a6d0ab0 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-c4090bef380a4eca8fe4f3ee9a6d0ab02025-01-19T06:24:34ZengElsevierEcological Informatics1574-95412025-03-0185102934Forecasting insect abundance using time series embedding and machine learningGabriel R. Palma0Rodrigo F. Mello1Wesley A.C. Godoy2Eduardo Engel3Douglas Lau4Charles Markham5Rafael A. Moral6Hamilton Institute, Maynooth University, Maynooth, Ireland; Department of Mathematics and Statistics, Maynooth University, Maynooth, Ireland; Corresponding author at: Hamilton Institute, Maynooth University, Maynooth, Ireland.Mercado Livre, Osasco, BrazilDepartment of Entomology and Acarology, University of São Paulo, Piracicaba, BrazilDepartment of Entomology and Acarology, University of São Paulo, Piracicaba, BrazilBrazilian Agricultural Research Corporation (Embrapa Trigo), Passo Fundo, Rio Grande do Sul, BrazilHamilton Institute, Maynooth University, Maynooth, Ireland; Department of Computer Science, Maynooth University, Maynooth, IrelandHamilton Institute, Maynooth University, Maynooth, Ireland; Department of Mathematics and Statistics, Maynooth University, Maynooth, IrelandImplementing insect monitoring systems provides an excellent opportunity to create accurate interventions for insect control. However, selecting the appropriate time for an intervention is still an open question due to the inherent difficulty of implementing on-site monitoring in real-time. A possible solution to enhance decision-making is to apply forecasting methods to predict insect abundance. However, another layer of complexity is added when other covariates are considered in the forecasting, such as climate time series collected along the monitoring system. Multiple combinations of climate time series and their lags can be used to build a forecasting method. Therefore, we propose a new approach to address this problem by combining statistics, machine learning, and time series embedding. We used two datasets containing a time series of aphids and climate data collected weekly in two municipalities in Southern Brazil for eight years. We conduct a simulation study based on a probabilistic autoregressive model with exogenous time series based on Poisson and negative binomial distributions to evaluate the performance of our approach. We pre-processed the data using our newly proposed approach and more straightforward approaches commonly used to train learning algorithms. We evaluate the performance of the selected algorithms by looking at the Pearson correlation and Root Mean Squared Error obtained using one-step-ahead forecasting. Based on Random Forests, Lasso-regularised linear regression, and LightGBM regression algorithms, we showed the feasibility of our novel approach, which yields competitive forecasts while automatically selecting insect abundances, climate time series and their lags to aid forecasting.http://www.sciencedirect.com/science/article/pii/S157495412400476XInsect outbreakIntegrated pest managementMachine learningForecastingCausality |
| spellingShingle | Gabriel R. Palma Rodrigo F. Mello Wesley A.C. Godoy Eduardo Engel Douglas Lau Charles Markham Rafael A. Moral Forecasting insect abundance using time series embedding and machine learning Ecological Informatics Insect outbreak Integrated pest management Machine learning Forecasting Causality |
| title | Forecasting insect abundance using time series embedding and machine learning |
| title_full | Forecasting insect abundance using time series embedding and machine learning |
| title_fullStr | Forecasting insect abundance using time series embedding and machine learning |
| title_full_unstemmed | Forecasting insect abundance using time series embedding and machine learning |
| title_short | Forecasting insect abundance using time series embedding and machine learning |
| title_sort | forecasting insect abundance using time series embedding and machine learning |
| topic | Insect outbreak Integrated pest management Machine learning Forecasting Causality |
| url | http://www.sciencedirect.com/science/article/pii/S157495412400476X |
| work_keys_str_mv | AT gabrielrpalma forecastinginsectabundanceusingtimeseriesembeddingandmachinelearning AT rodrigofmello forecastinginsectabundanceusingtimeseriesembeddingandmachinelearning AT wesleyacgodoy forecastinginsectabundanceusingtimeseriesembeddingandmachinelearning AT eduardoengel forecastinginsectabundanceusingtimeseriesembeddingandmachinelearning AT douglaslau forecastinginsectabundanceusingtimeseriesembeddingandmachinelearning AT charlesmarkham forecastinginsectabundanceusingtimeseriesembeddingandmachinelearning AT rafaelamoral forecastinginsectabundanceusingtimeseriesembeddingandmachinelearning |