Risk assessment of corn borer based on feature optimization and weighted spatial clustering: a case study in Shandong Province, China.

ABSTRACT: As a typical pest affecting corn yield and safety, corn borer causes serious economic losses worldwide. Climate warming has intensified the occurrence of pest outbreaks in recent years, but the associated risk has not been precisely assessed or understood. To address this gap, this paper took Shandong Province, China as a case study, and constructed a feature optimization model for the class imbalance problem and a novel risk assessment method to quantify the temporal and spatial distribution of corn borer occurrence risk. Addressing the prevalent issue of class imbalance in pest datasets, a feature optimization model using Borderline-SMOTE to improve the Genetic Algorithm-Random Forest (GA-RF) was constructed, combined with Pearson correlation coefficient to jointly obtain a subset of fea

SUBMITTER: Zuo Y 

PROVIDER: S-EPMC12313953 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature