随机森林转型模型动态和参数估计通过深度学习
Satoshi Kumabe1, Tianyu Song2, Tôn Việt Tạ1,2,3
1Joint Graduate School of Mathematics for Innovation, Kyushu University, 744 Motooka Nishi Ward, Fukuoka 819-0395, Japan.
Mathematical biosciences and engineering : MBE
|April 29, 2025
概括
这项研究使用随机微分方程和用于参数估计的新型深度学习方法来建模森林过渡,从而提高对森林砍伐动态的理解.
科学领域:
- 生态动力学 生态动力学
- 环境建模环境建模
- 机器学习应用程序 机器学习应用程序
背景情况:
- 森林转型涉及森林,农业和废弃土地之间的复杂转变.
- 了解这些动态对于解决森林砍伐至关重要.
- 现有模型在参数估计方面可能面临挑战.
研究的目的:
- 开发一个强大的森林转型动态模型.
- 为参数估计引入一种新的深度学习方法.
- 分析影响森林砍伐激励的因素.
主要方法:
- 开发了一种用于森林转变的随机微分方程模型.
- 使用数值分析来评估对森林砍伐的参数影响.
- 提出了一种深度学习方法,用于从时间序列数据中估计模型参数.
主要成果:
- 确定了模型的全球积极解决方案的存在.
- 确定了影响森林砍伐激励措施的关键模型参数.
- 证明了深度学习方法对参数估计的有效性.
结论:
- 开发的模型为理解森林转型的复杂性提供了一个框架.
- 深度学习方法提供了一种有效的参数估计方法.
- 这项研究提高了预测未来森林砍伐趋势的能力.
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