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Predicting the Activity Level of the Great Gerbil (Rhombomys opimus) via Machine Learning

文献类型： 外文期刊

作者： Jiang, Fan 1 ; Peng, Peng 1 ; Xu, Zhenting 1 ; Xu, Yu 1 ; Yang, Ding 1 ; Chai, Shouquan 1 ; Yuan, Shuai 2 ; Hua, Limin 3 ; Wang, Dawei 4 ; Wen, Xuanye 1 ;

作者机构： 1.Natl Forestry & Grassland Adm, Ctr Biol Disaster Prevent & Control, Shenyang, Peoples R China

2.Inner Mongolia Agr Univ, Coll Grassland Resources & Environm, Hohhot, Peoples R China

3.Gansu Agr Univ, Coll Grassland Sci, Minist Educ, Key Lab Grassland Ecosyst, Lanzhou, Peoples R China

4.Chinese Acad Agr Sci, Western Agr Res Ctr, Changji, Peoples R China

5.Chinese Acad Agr Sci, Inst Plant Protect, State Key Lab Biol Plant Dis & Insect Pests, Beijing, Peoples R China

关键词： extreme learning machine; machine learning; neural networks; population monitoring; Rhombomys opimus

期刊名称：ECOLOGY AND EVOLUTION （ 影响因子：2.3； 五年影响因子：2.8 ）

ISSN： 2045-7758

年卷期： 2025 年 15 卷 5 期

页码：

收录情况： SCI

摘要： The great gerbil (Rhombomys opimus) is a pest rodent that is widely distributed in Eurasia, and assessing its outbreak risk and instituting timely population control are very important for protecting vegetation and human health. Because traditional assessment methods are difficult to monitor and cannot effectively predict the population growth trend of R. opimus, an R. opimus activity prediction model was constructed using the particle swarm optimization algorithm-extreme learning machine (PSO-ELM). First, data for 13 factors influencing R. opimus growth, such as those related to the environment, vegetation, and activity in the previous year, at 46 R. opimus monitoring sites in China from 2020 to 2022 were selected. Second, principal component analysis was used to reduce the dimensionality of the 92 sets of collected data to six principal components, thus eliminating the correlation between the indicators. Third, after dimensionality reduction, the data were divided into a training set (80 sets of data) and a test set (12 sets of data) for model training and simulation, and the prediction results of the PSO-ELM model and back propagation model were compared. The simulation results revealed that the PSO-ELM model has a stronger convergence ability and higher prediction accuracy for the activity level of R. opimus in fall (91.67%). In this study, a new method is provided for surveying pest rodents. The proposed method provides an auxiliary means of managing R. opimus. We will continue to improve the sample data in future work to obtain more accurate predictions.

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