Stochastic forest transition model dynamics and parameter estimation via deep learning

2025-07-29

AI Summary

Modeling dynamic transitions among forest, agricultural land, and abandoned cropland is challenging due to complex nonlinearities and stochasticity; conventional approaches require extensive observational data for parameter estimation. Method: We propose a novel hybrid framework integrating stochastic differential equations (SDEs) with deep learning. Our SDE model explicitly captures state-dependent land-use transitions and environmental stochasticity, while an end-to-end deep neural network enables full model parameter inference from a single time series. Contribution/Results: We prove global existence and positivity of the SDE solution. Numerical experiments demonstrate accurate reconstruction of multi-phase deforestation trajectories and substantially improved long-term ecological forecasting. Unlike traditional statistical models, our approach eliminates reliance on dense spatiotemporal observations, offering an interpretable, generalizable, and data-efficient quantitative tool for elucidating forest transition mechanisms and informing sustainable land-use policy.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Sustainability of Web economics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Modeling complex forest-agriculture land transition dynamics
• Estimating parameters from time-series land proportion data
• Predicting future deforestation trends using deep learning

Innovation

• Stochastic differential equation model for forest transitions
• Deep learning for parameter estimation from time-series data
• Global positive solutions and numerical impact analysis