多层感知器与logistic回归(MLP-LR)相结合的方法较好地预测了中国棘球蚴(鳞翅目:棘球蚴科)的传播。

Hongwei Zhou, Zihan Xu, Yifan Chen, Yunbo Yan, Siyan Zhang, Xiao Lin, Di Cui, Jun Yang
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引用次数: 0

摘要

中国棘球绦虫(鳞翅目:棘球绦虫科)是严重威胁中国森林和农作物的害虫之一。其传播受多种因素的影响,包括环境因素和人为因素,现有的害虫传播数据与影响因素呈非线性关系。此外,虫害数据的收集往往受到限制,导致数据集较小,缺乏长期时间序列数据,以及数据缺失和异常等问题。传统的模型驱动方法在处理非线性关系和高维数据方面存在局限性,而数据驱动方法往往缺乏可解释性,容易出现过拟合,最终导致预测精度不足。因此,本文提出了MLP-LR方法,该方法将逻辑回归(LR)与多层感知器(MLP)相结合来克服这些局限性。该模型还采用贝叶斯自适应套索方法选择重要影响因素,进一步提高了预测精度。本研究基于中国大陆的H. cunea产状数据,验证了MLP-LR模型在小数据集上的稳定性和准确性。结果表明,与传统LR模型和独立的MLP模型相比,MLP-LR模型在预测美国血吸虫传播方面具有更好的效果,有效地解决了传统方法的不足。本研究为美国血吸虫病疫情的预测预警提供了新的工具和视角,为今后的研究和应用提供了重要参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The combined multilayer perceptron and logistic regression (MLP-LR) method better predicted the spread of Hyphantria cunea (Lepidoptera: Erebidae).

Hyphantria cunea (Lepidoptera: Erebidae) is one of the pests that pose a serious threat to forest and agronomic crops in China. Its spread is influenced by various factors, including environmental factors and anthropogenic factors, and the available data on pest spread and the influencing factor has nonlinear relationship. Additionally, the collection of pest data is often constrained, resulting in small datasets, a lack of long-term time series data, and issues such as missing data and anomalies. Traditional model-driven approaches have limitations in handling nonlinear relationships and high-dimensional data, while data-driven methods often lack interpretability and are prone to overfitting, ultimately leading to insufficient prediction accuracy. Therefore, this paper proposes the MLP-LR method, which combines logistic regression (LR) with a multilayer perceptron (MLP) to overcome these limitations. The model also used the Bayesian adaptive lasso method to select important influencing factors, that further improved the prediction accuracy. Based on H. cunea occurrence data in China, the current study demonstrated the stability and accuracy of the MLP-LR model on small datasets. The results showed that compared to traditional LR models and MLP independently, MLP-LR performs better in predicting the spread of H. cunea, effectively addressing the shortcomings of traditional methods. This study provides a new tool and perspective for forecasting and early warning of H. cunea outbreaks, offering important references for future research and its applications in the field.

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