Machine Learning-Based Prediction of Insect Damage Spread Using Auto-ARIMA Model

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dc.contributor.authorAlkan, Ece
dc.contributor.authorAydin, Abdurrahim
dc.date.accessioned2025-10-11T20:47:37Z
dc.date.available2025-10-11T20:47:37Z
dc.date.issued2024
dc.departmentDüzce Üniversitesien_US
dc.description.abstractDifferentiating areas of insect damage in forests from areas of healthy vegetation and predicting the future spread of damage increase are an important part of forest health monitoring. Thanks to the wide coverage and temporal observation advantage of remote sensing data, predicting the future direction of insect damage spread can enable accurate and uninterrupted management and operational control to minimize damage. However, due to the large amount of remotely sensed data, it is difficult to process the data and to identify damage distinctions. Therefore, this paper proposes a spatio-temporal Autoregressive Integrated MovingAverage (ARIMA) prediction model based on the Machine Learning technique for processing big data by monitoring oak lace bug (Corythucha arcuata (Heteroptera: Tingidae)) damage with remote sensing data. The advantage of this model is the automatic selection of optimal parameters to provide better forecasting with univariate time series. Thus, multiple spatiotemporal warning levels are distinguished according to the damage growth trend in the series, and the network is constructed with improved time series to better predict future insect damage spread. In the proposed model, the historical Red (R) - Green (G) - Blue (B) bands of the Sentinel-2 (GSD 10 m) satellite were tested as a dataset for the oak lace bug damage in the oak forest situated in the campus of Duzce University, Turkey. The dataset, which contained 38 images for each of the RGB bands, was modeled using the open source R programming language for the peak damage period in 2021. As a result of the test, significant correlations were found between the synthetic and true images (True and synthetic band 2: r=0.960, p<0.001; True and synthetic band 3: r=0.945, p<0.001; True and synthetic band 4: r=0.962, p<0.001). Then, the 48-month time series bands were modeled, and the band estimates were made to predict the August 2023 spread. Finally, a synthetic composite image was created for future prediction using the predicted bands. The tests showed that the model had a good performance in insect damage monitoring. With open access Sentinel-2 images, the proposed model achieved the highest prediction accuracy with a rate of 96%, and had a small prediction error.en_US
dc.description.sponsorshipDUBAP project [2022.02.02.1351]en_US
dc.description.sponsorshipDuezce University Scientific Research Projects Coordinatorshipen_US
dc.description.sponsorshipThis article is derived from a PhD dissertation con- ducted by the lead author under the supervision of the second author at Duezce University, Institute of Graduate Studies, Department of Forest Engineering. Also, the thesis work was supported by DUBAP project No. 2022.02.02.1351 >> Artificial Intelligence Based Detection of Insect Damage in Forests with Remote Sensing Data <<. For this reason, we would like to thank Duezce University Scientific Research Projects Coordinatorship for providing financial support for the thesis study. Special thanks to Prof. Ahmet MERT who supported the development of this article idea.en_US
dc.identifier.doi10.5552/crojfe.2024.2299
dc.identifier.endpage364en_US
dc.identifier.issn1845-5719
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85211104467en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage351en_US
dc.identifier.urihttps://doi.org/10.5552/crojfe.2024.2299
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21474
dc.identifier.volume45en_US
dc.identifier.wosWOS:001277221400012en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherZagreb Univ, Fac Forestryen_US
dc.relation.ispartofCroatian Journal of Forest Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectremote sensingen_US
dc.subjectinsect damageen_US
dc.subjectmachine learningen_US
dc.subjectARIMA modelen_US
dc.titleMachine Learning-Based Prediction of Insect Damage Spread Using Auto-ARIMA Modelen_US
dc.typeArticleen_US

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