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Machine learning methods trained on simple models can predict critical transitions in complex natural systems

Smita Deb, Sahil Sidheekh, View ORCID ProfileChristopher F. Clements, Narayanan C. Krishnan, View ORCID ProfilePartha S. Dutta
doi: https://doi.org/10.1101/2021.03.15.435556
Smita Deb
1Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab 140 001 India
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Sahil Sidheekh
2Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab 140 001 India
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Christopher F. Clements
3School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK
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Narayanan C. Krishnan
2Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab 140 001 India
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  • For correspondence: parthasharathi@iitrpr.ac.in ckn@iitrpr.ac.in
Partha S. Dutta
1Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab 140 001 India
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  • For correspondence: parthasharathi@iitrpr.ac.in ckn@iitrpr.ac.in
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Abstract

1. Sudden transitions from one stable state to a contrasting state occur in complex systems ranging from the collapse of ecological populations to climatic change, with much recent work seeking to develop methods to predict these unexpected transitions from signals in time series data. However, previously developed methods vary widely in their reliability, and fail to classify whether an approaching collapse might be catastrophic (and hard to reverse) or non-catastrophic (easier to reverse) with significant implications for how such systems are managed.

2. Here we develop a novel detection method, using simulated outcomes from a range of simple mathematical models with varying nonlinearity to train a deep neural network to detect critical transitions - the Early Warning Signal Network (EWSNet).

3. We demonstrate that this neural network (EWSNet), trained on simulated data with minimal assumptions about the underlying structure of the system, can predict with high reliability observed real-world transitions in ecological and climatological data. Importantly, our model appears to capture latent properties in time series missed by previous warning signals approaches, allowing us to not only detect if a transition is approaching but critically whether the collapse will be catastrophic or non-catastrophic.

4. The EWSNet can flag a critical transition with unprecedented accuracy, overcoming some of the major limitations of traditional methods based on phenomena such as Critical Slowing Down. These novel properties mean EWSNet has the potential to serve as a universal indicator of transitions across a broad spectrum of complex systems, without requiring information on the structure of the system being monitored. Our work highlights the practicality of deep learning for addressing further questions pertaining to ecosystem collapse and have much broader management implications.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 16, 2021.
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Machine learning methods trained on simple models can predict critical transitions in complex natural systems
Smita Deb, Sahil Sidheekh, Christopher F. Clements, Narayanan C. Krishnan, Partha S. Dutta
bioRxiv 2021.03.15.435556; doi: https://doi.org/10.1101/2021.03.15.435556
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Machine learning methods trained on simple models can predict critical transitions in complex natural systems
Smita Deb, Sahil Sidheekh, Christopher F. Clements, Narayanan C. Krishnan, Partha S. Dutta
bioRxiv 2021.03.15.435556; doi: https://doi.org/10.1101/2021.03.15.435556

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