Stochastic forest transition model dynamics and parameter estimation via deep learning

TL;DR

This paper introduces a stochastic differential equation model for forest transition dynamics and proposes a deep learning method to estimate model parameters from time-series data, enhancing understanding of deforestation trends.

Contribution

It presents a novel stochastic model for forest transitions and a deep learning approach for parameter estimation from limited data samples.

Findings

Established global positive solutions for the model

Assessed parameter impacts on deforestation incentives

Demonstrated effective parameter estimation from single time-series samples

Abstract

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.