Learning to learn ecosystems from limited data -- a meta-learning approach

• URL: http://arxiv.org/abs/2410.07368v1
• Date: Wed, 2 Oct 2024 16:23:34 GMT
• Title: Learning to learn ecosystems from limited data -- a meta-learning approach
• Authors: Zheng-Meng Zhai, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai,
• Abstract summary: We develop a meta-learning framework with time-delayed feedforward neural networks to predict the long-term behaviors of ecological systems. We show that the framework is capable of accurately reconstructing the dynamical climate'' of the ecological system with limited data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A fundamental challenge in developing data-driven approaches to ecological systems for tasks such as state estimation and prediction is the paucity of the observational or measurement data. For example, modern machine-learning techniques such as deep learning or reservoir computing typically require a large quantity of data. Leveraging synthetic data from paradigmatic nonlinear but non-ecological dynamical systems, we develop a meta-learning framework with time-delayed feedforward neural networks to predict the long-term behaviors of ecological systems as characterized by their attractors. We show that the framework is capable of accurately reconstructing the ``dynamical climate'' of the ecological system with limited data. Three benchmark population models in ecology, namely the Hastings-Powell model, a three-species food chain, and the Lotka-Volterra system, are used to demonstrate the performance of the meta-learning based prediction framework. In all cases, enhanced accuracy and robustness are achieved using five to seven times less training data as compared with the corresponding machine-learning method trained solely from the ecosystem data. A number of issues affecting the prediction performance are addressed.

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