Forecasting Dendrolimus sibiricus Outbreaks: Data Analysis and Genetic Programming-Based Predictive Modeling-Reference-Cited by-同舟云学术

Forecasting Dendrolimus sibiricus Outbreaks: Data Analysis and Genetic Programming-Based Predictive Modeling

Published:2024-04-30 Issue:5 Volume:15 Page:800
ISSN:1999-4907
Container-title:Forests
language:en
Short-container-title:Forests

Author:

Malashin Ivan¹^ORCID,Masich Igor¹²³^ORCID,Tynchenko Vadim¹²³^ORCID,Nelyub Vladimir¹⁴^ORCID,Borodulin Aleksei¹^ORCID,Gantimurov Andrei¹,Shkaberina Guzel²³^ORCID,Rezova Natalya²³^ORCID

Affiliation:

1. Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia

2. Laboratory “Hybrid Methods of Modeling and Optimization in Complex Systems”, Siberian Federal University, 79 Svobodny Prospekt, 660041 Krasnoyarsk, Russia

3. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Prospekt, 660037 Krasnoyarsk, Russia

4. Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia

Abstract

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 modeling. 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.

Publisher

MDPI AG

Link

https://www.mdpi.com/1999-4907/15/5/800/pdf

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