Work

Applying Machine Learning Models to Predict Population Dynamics of Harmful Non-Gregarious Locusts Across Kazakhstan Agroclimatic Zones

Year: 2025

Type: article

Abstract: The paper reports the results of studies on the use of models based on machine learning algorithms and IT to predict the population dynamics of harmful, non-gregarious locusts in different agroclimatic zones of Kazakhstan. The research goal was to create a forecasting model for the population of harmful non-gregarious locusts in the agroclimatic zones of Kazakhstan using a machine learning algorithm and climatic predictors. The study covered the highly humid and slightly humid and moderately war

Source: World Journal of Environmental Biosciences

DOI: 10.51847/5oaisyhbcp

Authors: Kurmet Baibussenov, Zhassulan Amanbay, Aigul Bekbaeva, Valery Azhbenov, Arman Rustembayev

Institution: S.Seifullin Kazakh Agro Technical University

Language: English

Topic: Orthoptera Research and Taxonomy

Subfield: Ecology, Evolution, Behavior and Systematics

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Life on land, Zero hunger

Open Access status: hybrid

OpenAlex ID: W7160374875