用深度学习从单组数据估计森林动态模型参数，预测未来土地变化。
Stochastic forest transition model dynamics and parameter estimation via deep learning
- 构建随机微分方程模型模拟林地、农田、弃耕土地的动态转换。
- 提出新深度学习方法，仅需一组时间序列数据即可估计全部参数。
- 可预测未来森林退化趋势，适合环境政策与生态研究者使用。
森林转换表现为林地、农业用地和弃耕地之间的动态转变，过程复杂。本研究建立了一个随机微分方程模型以捕捉这些转换的复杂动态。我们证明了该模型存在全局正解，并通过数值分析评估了模型参数对砍伐激励的影响。针对参数估计难题，提出一种新型深度学习方法，可仅凭一组包含林地与农田比例时间序列的样本，同时估计所有模型参数。该方法使我们能够理解森林转换动态，并在任意未来时间点预测森林退化趋势。
原文摘要 · Abstract (English)
Forest transitions, characterized by dynamic shifts between forest, agricultural, and abandoned lands, are complex phenomena. This study developed a stochastic differential equation model to capture the intricate dynamics of these transitions. We established the existence of global positive solutions for the model and conducted numerical analyses to assess the impact of model parameters on deforestation incentives. To address the challenge of parameter estimation, we proposed a novel deep learning approach that estimates all model parameters from a single sample containing time-series observations of forest and agricultural land proportions. This innovative approach enables us to understand forest transition dynamics and deforestation trends at any future time.
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