In this Anthropocene epoch, increased human activities have led to unprecedented global environmental changes increasing the likelihood of sudden transitions. With an expected peak in global energy-related emissions, downpour of the world economy, and the expected risk of a sudden collapse in various complex systems, sustainability itself is at the verge of a tipping point. Amidst such risks, recent work is seeking to develop methods that detect such transitions from patterns in time series. Nevertheless, we are aware of the varying degree of reliability of traditional tools in anticipating a shift to and from contrasting stable states. Generic EWSs also fail to classify whether an approaching collapse might be catastrophic (critical) or non-catastrophic (smooth). Under this backdrop, we develop a novel machine learning (ML) based Early Warning Signal Network trained using sequence data generated from a range of simple mathematical models with varying nonlinearity - aimed at classifying critical transitions from smooth and no transitions. Our model is trained on ample raw time series data with minimal preassumptions about the underlying structure of the system.
Results/Conclusions
We demonstrate that our ML-based Early Warning Signal Network is capable of distinguishing transitions with high reliability in real ecological and climatological data. Apparently, a salient feature of the Early Warning Signal Network is its ability to capture latent properties in time series beyond critical slowing down that allows it to distinguish critically between the two classes of transitions that share the common attribute - CSD. Moreover, our proposed method provides robust predictions over traditional indicators on noisy time series with memory in fluctuations, imperfectly sampled, and inexplicably short sequences - some of the challenges facing predictions in real time series. While we present forth this novel method as a first-pass tool to prioritize at-risk systems, it has the potential to serve as a universal indicator of transitions across a broad spectrum of complex systems. With further finetuning over different labels across a range of mathematical models with larger parameter ranges, this will inevitably provide an increasingly robust and widely applicable framework into the future. Our work highlights the practicality of ML methods for addressing further questions pertaining to ecosystem collapse, will open up the possibility of real-time monitoring of many real-world systems, and have much broader management implications.
