建模方法的演变:从统计模型到用于虫和虫预测的深度学习
Insects
|February 27, 2026
概括
本综述探讨了用于预测虫和虫疫情的深度学习,这对粮食安全至关重要. 未来的工作应该整合可解释的人工智能,以实现更强大的生态预警系统.
科学领域:
- 生态生态学 生态生态学
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 虫和虫的爆发威胁到全球粮食安全和生态系统的稳定,特别是在草原.
- 由于环境因素之间的复杂的时空相互作用,预测这些害虫爆发是具有挑战性的.
- 虫比虫具有更大的空间尺度和连接性.
研究的目的:
- 审查虫和虫爆发预测方法的演变.
- 评估深度学习 (DL) 方法在生态预测中的应用和局限性.
- 突出DL模型适应草原生态系统和草等被忽视的物种.
主要方法:
- 传统的统计模型,经典的机器学习和深度学习架构 (DNN,CNN,RNN,LSTM,GRU) 的比较.
- 对DL模型在建模虫种群动态中的性能进行分析.
- 强调适应草原生态系统和虫爆发的DL模型.
主要成果:
- 深度学习方法显示出对昆虫爆发的生态预测有前途.
- 大多数研究都集中在虫上,在某些特定地区,如内蒙古,对虫的关注较少.
- 挑战包括数据稀缺性,有限的概括性和DL模型的低解释性.
结论:
- 解释性AI (XAI),转移学习和生成模型 (GAN) 被提议用于增强预测工具.
- 未来的研究应该专注于开发强大,透明和生态基础的早期预警系统.
- 像Gated Recurrent Units (GRUs) 这样的高效架构可以帮助脆弱生态系统的可持续害虫管理.
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