Stochastic forest transition model dynamics and parameter estimation via deep learning
This work addresses the problem of understanding and predicting complex forest dynamics for environmental researchers and policymakers, though it is incremental in applying deep learning to parameter estimation.
The study tackled modeling forest transitions by developing a stochastic differential equation model and proposed a novel deep learning method to estimate all parameters from a single time-series sample, enabling prediction of future deforestation trends.
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.