# Stochastic forest transition model dynamics and parameter estimation via deep learning

arXiv: https://arxiv.org/abs/2507.21486 · HTML: https://mlsanity.com/paper/2507.21486/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 15 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Built on by (most cited first, 2 of 2)

- [Deep Learning for Automated Identification of Vietnamese Timber Species: A Tool for Ecological Monitoring and Conservation](https://mlsanity.com/paper/2508.10938/) (2025)
- [BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations](https://mlsanity.com/paper/2605.12534/) (2026)

## Abstract (arXiv)

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.