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在Culex pipiens预测建模中比较机器学习,深度学习和强化学习的性能.

Wei Yin1, Sanad H Ragab2, Michael G Tyshenko3

  • 1School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.

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此摘要是机器生成的。

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科学领域:

  • 生态生态学 生态生态学
  • 机器学习 机器学习
  • 流行病学 流行病学

背景情况:

  • 机器学习 (ML) 和深度学习 (DL) 是为了预测物种存在而建立的.
  • 强化学习 (RL) 对于物种分布建模来说是新鲜的.
  • 曲力斯皮皮恩斯是西尼罗河病毒 (WNV) 的全球载体.

研究的目的:

  • 将ML/DL分类器与RL方法进行比较,以预测Culex pipiens的分布.
  • 使用美国历史存在数据评估不同算法的有效性.

主要方法:

  • 后勤回归的逻辑回归
  • 随机的森林分类器是随机的森林分类器.
  • 深度神经网络是一种深度神经网络.
  • RL算法:Q学习,深度Q网络 (DQN),REINFORCE,演员批判

主要成果:

  • 所有测试方法在预测物种分布方面表现相似.
  • 在较少的特征下,RL方法 (DQN,REINFORCE) 是有效的.
  • 高度和年降水量是最重要的生物气候预测指标.

结论:

  • RL方法为物种分布建模提供了一个有希望的替代方案,特别是在数据有限或动态场景中.
  • 诸如海拔和降水等生物气候变量对于了解Culex pipiens的分布至关重要.