A Method based on Meteorological Data for Predicting the Occurrence of Micromelalopha sieversi
SONG Tao
QI Yukun
YAN Jiahe
SUN Xiaoling
FU Jin
ZHANG Lizhong
QIU Yue
LIANG Yuting
ZHOU Lingxi
WANG Tao
KANG Zhi
WANG Qinghai
YU Lianjia
Abstract：To rapidly and accurately predict the impact of meteorological factors on the population dynamics of Micromelalopha sieversi,this study proposes a Long Short-Term Memory(LSTM)-based prediction model driven by meteorological data.By integrating daily meteorological data and pest monitoring records from 2014 to 2022 in Shanghe County,Jinan City,Shandong Province,a"meteorology-pest severity"mapping dataset was constructed,where pest occurrence levels were categorized into 0-3 grades(thresholds:0,100,1000 individuals).Through Extreme Gradient Boosting(XGBoost)-based feature selection,20 critical meteorological factors(e.g.,winter average temperature and consecutive rainless days)significantly influencing pest severity were identified.The model was trained and evaluated using five-fold time-series cross-validation.Results demonstrated that the LSTM model significantly outperformed baseline models(Logistic Regression,Random Forest,and LightGBM),achieving an accuracy of 0.8420,weighted precision of 0.8442,weighted recall of 0.8420,weighted F1-score of 0.8367.Further validation on an independent 2023 test set highlighted its superior short-term forecasting capability(87.78%accuracy in the first quarter).This study establishes a high-precision methodological framework for time-series prediction of forestry pest outbreaks,offering critical insights for optimizing eco-friendly pest control strategies.
Keywords：meteorological dataforestry pestsfeature selectionlong short-term memory
Publication Date：2025-12-28
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：8( 34-40,45 )