Тип публикации: статья из журнала

Год издания: 2024

Идентификатор DOI: 10.3390/f15050800

Аннотация: <jats:p>This study presents an approach to forecast outbreaks of Dendrolimus sibiricus, a significant pest affecting taiga ecosystems. Leveraging comprehensive datasets encompassing climatic variables and forest attributes from 15,000 taiga parcels in the Krasnoyarsk Krai region, we employ genetic programming-based predictive modelПоказать полностьюing. Our methodology utilizes Random Forest algorithm to develop robust forecasting model through integrated data analysis techniques. By optimizing hyperparameters within the predictive model, we achieved heightened accuracy, reaching a maximum precision of 0.9941 in forecasting pest outbreaks up to one year in advance.</jats:p>

Журнал: Forests

Выпуск журнала: Т. 15, № 5

Номера страниц: 800

ISSN журнала: 19994907

• Malashin Ivan (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)
• Masich Igor (Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Prospekt, 660037 Krasnoyarsk, Russia)
• Tynchenko Vadim (Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Prospekt, 660037 Krasnoyarsk, Russia)
• Nelyub Vladimir (Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia)
• Borodulin Aleksei (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)
• Gantimurov Andrei (Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia)
• Shkaberina Guzel (Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Prospekt, 660037 Krasnoyarsk, Russia)
• Rezova Natalya (Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Prospekt, 660037 Krasnoyarsk, Russia)

• Ядро РИНЦ (eLIBRARY.RU)