在一个离散的捕食者-猎物模型中,机器学习和分叉分析与因尼姆诱导的死亡率
Tayyaba Mehmood1, Muhammad Rafaqat1, Salman Saleem2,3
1Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan.
Scientific reports
|November 19, 2025
概括
这项研究使用掠食者-猎物系统来模拟瓜害虫控制. 最佳的neem应用和干预时间对于稳定的害虫管理至关重要,机器学习有助于稳定性分析.
科学领域:
- 生态生态学 生态生态学
- 数学生物学 数学生物学
- 计算科学 计算科学
背景情况:
- 瓜生产面临着重大害虫挑战,需要有效和可持续的控制策略.
- 捕食者-猎物模型是了解和管理农业系统害虫动态的宝贵工具.
研究的目的:
- 开发和分析一个离散时间捕食者-猎物模型,用于瓜害虫防治.
- 调查因尼姆产生的死亡率和干预频率对系统稳定性的影响.
- 探索机器学习在近似模型稳定性区域中的应用.
主要方法:
- 一个离散时间的捕食者-猎物模型,包括后勤猎物增长,尼姆效应和捕食者干扰,使用断片常数论证 (PCA) 方案来制定.
- 使用分析技术 (分叉分析,利亚普诺夫指数) 和数值模拟 (分叉图) 来确定稳定性条件.
- 机器学习分类器 (随机森林,决策树) 用于近似分析推导的稳定性区域.
主要成果:
- 该模型确定了翻转和Neimark-Sacker分叉的条件,揭示了复杂的人口动态.
- 在生态上,低的尼姆诱导的死亡率会破坏共存的稳定性,而更高的水平会恢复稳定性;干预频率会对稳定性产生重大影响,而适度的使用会促进稳定性.
- 机器学习模型准确地复制了稳定性图,随机森林显示出卓越的性能,展示了ML作为计算替代品的潜力.
结论:
- 可持续的果害虫防治取决于平衡的果应用,战略性干预时间和自然捕食者的保护.
- 该研究强调了生态因素和控制策略之间的相互作用,为综合性害虫管理提供了见解.
- 机器学习为高效分析复杂的生态模型和指导害虫管理决策提供了一个有希望的途径.
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