Rare events in a stochastic vegetation-water dynamical system based on machine learning.

Abstract

Stochastic vegetation-water dynamical systems are fundamental to understanding ecological stability, biodiversity conservation, water resource sustainability, and climate change adaptation. In this study, we introduce an innovative machine learning framework for analyzing rare events in stochastic vegetation-water systems driven by multiplicative Gaussian noise. By integrating the Freidlin-Wentzell large deviation theory with deep learning techniques, we establish rigorous asymptotic formulations for both the quasipotential and the mean first exit time. Leveraging vector field decomposition principles, we develop a novel neural network architecture capable of accurately computing the most probable transition paths and mean first exit times across diverse boundary conditions, including both non-characteristic and characteristic scenarios. Our findings demonstrate that the proposed method significantly enhances the predictive capabilities for early detection of vegetation degradation, thereby offering robust theoretical foundations and advanced computational tools for ecological management and conservation strategies. Furthermore, this approach establishes a scalable framework for investigating more complex, high-dimensional stochastic dynamical systems, opening new avenues for research in ecological modeling and environmental forecasting.