The spread and misclassification prediction model of Vespa mandarinia based on TD-IDF and SVM

Leiluo Ouyang; Yujie Xu; Heming Xie

doi:10.1117/12.2641810

10 November 2022 The spread and misclassification prediction model of Vespa mandarinia based on TD-IDF and SVM

Leiluo Ouyang, Yujie Xu, Heming Xie

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 1234815 (2022) https://doi.org/10.1117/12.2641810
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

To address the problem that emerged in Washington State regarding hornets, we propose the Vespa Mandarinia Spread model for analysis and prediction of the dispersal characteristics of hornets. We substituted the processed data into the SARIMA model to obtain the predicted spread of hornets in Washington State for the next six months. The predicted results fit well with the actual data. To interpret and predict the correctness of the sighting reports, we propose the Misclassification Likelihood Prediction model to analyze and find the relationship between the content of the sighting reports and the likelihood of misclassification. We classified the text content with the TD-IDF algorithm. Then, we trained a linear Support Vector Machine (SVM) model to predict the probability of misclassification. The prediction results of the model showed that 98% of the sightings were misclassified, and only 2% of the sightings were accurate.

Citation Download Citation

Leiluo Ouyang, Yujie Xu, and Heming Xie "The spread and misclassification prediction model of Vespa mandarinia based on TD-IDF and SVM", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 1234815 (10 November 2022); https://doi.org/10.1117/12.2641810

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