Deep learning for early warning signals of tipping points
Open accessAbstract
Significance: Early warning signals (EWS) of tipping points are vital to anticipate system collapse or other sudden shifts. However, existing generic early warning indicators designed to work across all systems do not provide information on the state that lies beyond the tipping point. Our results show how deep learning algorithms (artificial intelligence) can provide EWS of tipping points in real-world systems. The algorithm predicts certain qualitative aspects of the new state, and is also more sensitive and generates fewer false positives than generic indicators. We use theory about system behavior near tipping points so that the algorithm does not require data from the study system but instead learns from a universe of possible models.
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“We also experimented with multilayer perceptron 33 , long short-term memory 14,46 and self-attention 47 architectures for the DL model.”
Early warning signals for critical transitions in complex systems
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Predicting discrete-time bifurcations with deep learning
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Self-citationUniversal early warning signals of phase transitions in climate systems
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“Recent results in deep learning methods for EWS detection have supported this claim, demonstrating that models trained on simple synthetic dynamics can be successfully transferred to empirical climate data from various sources [25, 26].”
BackgroundMethodResultInfluentialSelf-citationA resilience sensing system for the biosphere
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“Deep learning can use these to distinguish the type of tipping point being approached and provide improved early warning [48].”
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Bridging adaptive management and reinforcement learning for more robust decisions
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“[15] use machine learning to classify critical transitions into four possible classes but do not suggest action strategies given a belief state in the system dynamics.”
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BackgroundWhy topological data analysis detects financial bubbles?
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Machine learning prediction of tipping in complex dynamical systems
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“47,48).”
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“A different approach leverages deep learning to identify, in addition, whether the approaching transition is fold, Hopf, or transcritical bifurcation [33].”
BackgroundOverlapping timescales obscure early warning signals of the second COVID-19 wave
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“Boettiger & Hastings [46] have shown that statistically comparing two models, one that includes the bifurcation and one that does not, can outperform nonparametric testing using null models (see also [47]).”
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“The optimization of composite indicators further points to the use of machine learning methods when abundant data are available, 95 but also opens important caveats for their application in real life: feature combinations may be optimized for certain settings (e.g., noise intensity or type) but may…”
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Detecting the critical states during disease development based on temporal network flow entropy
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Limits to ecological forecasting: Estimating uncertainty for critical transitions with deep learning
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“…developed for classification rather than the ML methods used in regression and forecasting models considered here have demonstrated a more nuanced ability to reliably detect different classes of critical transitions in time- series data (Bury et al., 2021; Lapeyrolerie & Boettiger, 2021).”
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Extreme events in biological, societal, and earth sciences: A systematic review of the literature
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“varied as epileptic seizures and the Earth’s climate (Bury et al., 2021), based largely on studying rates of change over time in one or more variables.”
BackgroundTime-series-analysis-based detection of critical transitions in real-world non-autonomous systems
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“High AUC values for various detection methods continue to be reported from different disciplines 138,153,162,232,243–251 .”
BackgroundMulticlass Classification Performance Curve
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Negative social tipping dynamics resulting from and reinforcing Earth system destabilization
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The Current Landscape of Early Warning Systems and Traditional Approaches to Disaster Detection
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Vegetation dynamics: Modeling, mechanisms, and emergent properties
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ewstools: A Python package for early warning signals of bifurcations in time series data
Bury, T. M. (2023) · The Journal of Open Source Software · doi:10.21105/joss.05038 · Cited by 17 · see citing works
“…the recent surge in popularity of the Python programming language (PYPL, 2022), a Python-based implementation of EWS should be useful. ewstools also implements novel deep learning methods for EWS, which have outperformed conventional EWS in several model and empirical systems (Bury et al., 2021).”
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Destabilization of Earth system tipping elements
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“Promising advances include transfer operator approaches 128 and emerging machine learning techniques that can detect informative changes in time-series data beyond CSD-based indicators 129–131 .”
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Non-Normal Interactions Create Socio-Economic Bubbles
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Improved GRU prediction of paper pulp press variables using different pre-processing methods
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“Recent research has used the LOWESS smoothing technique in order to optimize the process of training and testing deep neural networks (Bury et al., 2021; Kulkarni et al., 2021).”
MethodMixtures Recomposition by Neural Nets: A Multidisciplinary Overview
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Early warnings for multi-stage transitions in dynamics on networks
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Tipping points in coupled human–environment system models: a review
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“Much research has been done in the past few decades to develop tools for EWSs using both empirical and synthetic time series data (Bury et al., 2021; Dakos et al., 2012, 2015, 2008; Kéfi et al., 2014; Lapeyrolerie and Boettiger, 2021).”
MethodSelf-citationEarly warning for spatial ecological system: Fractal dimension and deep learning
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“There has been an increased interest in the analysis and detection of bifurcation-induced transitions [7, 5, 9, 10, 34, 14].”
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“Furthermore, spillovers were more prominent during turmoil or crisis episodes, especially the global financial crisis and the COVID-19 pandemic.”
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“The DNB theory [15] detects the early warning signals [16–23] of state transitions, specifically the unstable states, in complex networks of biological systems.”
BackgroundModelling induced innovation for the low-carbon energy transition: a menu of options
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“Depending on the internal dynamic assumptions included in a model, DSMs can be suitable for addressing non-linear change and the outcomes of passing thresholds and tipping points [16, 17, 136].”
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An early warning indicator trained on stochastic disease-spreading models with different noises
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“[20].”
InfluentialB-tipping points in plankton dynamics: Stochasticity and early warning signals
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Deep learning for tipping points: Preprocessing matters
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“(1) present a powerful approach to anticipating tipping points based on deep learning that not only substantially outperforms traditional early warning indicators but also classifies the type of bifurcation that may lie ahead.”
BackgroundMethodInfluentialSelf-citationEffect of homophily on coupled behavior-disease dynamics near a tipping point
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Self-citationFateNet: an integration of dynamical systems and deep learning for cell fate prediction
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“In particular, a neural network can learn to predict bifurcations by training it on a massive corpus of simulation data from dynamical systems with noise (Bury et al. 2021).”
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“Natural science knowledge on tipping points is expanding quickly, due to new algorithms predicting tipping point and post-tipping point states (Bury et al., 2021).”
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Accelerating transformations for a just, sustainable future: 10 ‘Must Haves’
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“From that foundation, the capacities to detect, prevent, and respond to potential crises must be developed and invested in (Bauch et al., 2016; Bury et al., 2021; Lenton, 2020); protocols to recognize the spillover effects of strife and collapse across scales must be institutionalized; social…”
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Phylogenomic early warning signals for SARS-CoV-2 epidemic waves
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AI-Powered Satellite Imagery Processing for Global Air Traffic Surveillance
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Early warnings are too late when parameters change rapidly
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Self-citationCritical Transitions and Short-Term Warning Signals of Seismic Rockfall Systems
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“The previous research has commonly employed variance and AC to characterize this slowdown (Bury et al. 2021), which has been widely applied as a warning method in fields, such as ecosystems (Dakos, et al. 2015; van Belzen et al. 2017), climate change (Dakos et al. 2008; Lenton et al. 2012), and…”
Relaxation time as early warning signal of avalanches in self-organizing systems
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Perspectives on the timing of ecosystem collapse in a changing climate
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“…original state could occur in different ways: fold bifurcation, which corresponds to an abrupt change; transcritical bifurcation, which usually causes a smooth transition; or Holpf bifurcation, which is an oscillatory behavior of the ecosystem between different alternate states (Bury et al., 2021).”
Teaching machines to anticipate catastrophes
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“Such networks have important implications for society as model systems to study tipping points in early-warning theories [26, 32–34] that pertain, for example, to climate models, avalanches, and economy.”
Predicting critical transitions with machine learning trained on surrogates of historical data
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“In recent years, deep learning classi fi ers have been employed for early warningsignals 37,53 – 57 .”
InfluentialSelf-citationDeep learning for predicting the occurrence of tipping points
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“Therefore, several methods such as DEV [12] or some DL classifiers [18,22–24] should be used to determine whether a system is approaching a tipping point first.”
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“8] and to period one in interval A ∈ [28 .”
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Unveiling critical transition in a transport network model: stochasticity and early warning signals
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Tipping point for pulsatile oscillations in dynamical networks
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Do early warning signals of tipping points lead to better decisions?
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Early warning signals for bifurcations embedded in high dimensions
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“The neural EWS detection model used in this work is based on that of Bury et al. 11 , code for which is available at https:// github. com/ Thoma sMBury/ deep- early-warni ngs-pnas.”
BackgroundMethodInfluentialSelf-citation
- Authors
- Bury, T. M., Sujith, R. I., Pavithran, I., Scheffer, M., Lenton, T. M., Anand, M., & Bauch, C. T.
- Affiliations
- University of Waterloo (CA)
- Indian Institute of Technology Madras (IN)
- Wageningen University & Research (NL)
- University of Exeter (GB)
- University of Guelph (CA)
- Corresponding author
- Bauch, Chris T.
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2021-09-20
- Volume
- 118
- Issue
- 39
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC8488604
- OpenAlex
- W3200303296
- Semantic Scholar
- 91c6031240527a1cdba25aef61bdf5a3cf767498
- Cited by
- 284
- References
- 54
- Citations by year
- 2026: 61 · 2025: 59 · 2024: 67 · 2023: 51 · 2022: 44 · 2021: 2
- FWCI
- 16.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (submittedVersion, cc-by-nc-nd)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
- Funders
- Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (RGPIN 2019, RGPIN 2020)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Ecosystem dynamics and resilience
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Chaos control and synchronization
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- tipping points, early warning indicators, normal forms, dynamical systems, deep learning, state transitions, lag-1 autocorrelation, variance, scaling behavior, model-free prediction, ecological tipping points, thermoacoustics, climate tipping points
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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