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Author: Havlin, Shlomo × Type: Article ×

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    Teleconnections among tipping elements in the Earth system
    (London : Nature Publ. Group, 2023) Liu, Teng; Chen, Dean; Yang, Lan; Meng, Jun; Wang, Zanchenling; Ludescher, Josef; Fan, Jingfang; Yang, Saini; Chen, Deliang; Kurths, Jürgen; Chen, Xiaosong; Havlin, Shlomo; Schellnhuber, Hans Joachim
    Tipping elements are components of the Earth system that may shift abruptly and irreversibly from one state to another at specific thresholds. It is not well understood to what degree tipping of one system can influence other regions or tipping elements. Here, we propose a climate network approach to analyse the global impacts of a prominent tipping element, the Amazon Rainforest Area (ARA). We find that the ARA exhibits strong correlations with regions such as the Tibetan Plateau (TP) and West Antarctic ice sheet. Models show that the identified teleconnection propagation path between the ARA and the TP is robust under climate change. In addition, we detect that TP snow cover extent has been losing stability since 2008. We further uncover that various climate extremes between the ARA and the TP are synchronized under climate change. Our framework highlights that tipping elements can be linked and also the potential predictability of cascading tipping dynamics.
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    Topology of products similarity network for market forecasting
    ([Cham] : Springer International Publishing, 2019) Fan, Jingfang; Cohen, Keren; Shekhtman, Louis M.; Liu, Sibo; Meng, Jun; Louzoun, Yoram; Havlin, Shlomo
    The detection and prediction of risk in financial markets is one of the main challenges of economic forecasting, and draws much attention from the scientific community. An even more challenging task is the prediction of the future relative gain of companies. We here develop a novel combination of product text analysis, network theory and topological based machine learning to study the future performance of companies in financial markets. Our network links are based on the similarity of firms’ products and constructed using the Securities Exchange Commission (SEC) filings of US listed firms. We find that several topological features of this network can serve as good precursors of risks or future gain of companies. We then apply machine learning to network attributes vectors for each node to predict successful and failing firms. The resulting accuracies are much better than current state of the art techniques. The framework presented here not only facilitates the prediction of financial markets but also provides insight and demonstrates the power of combining network theory and topology based machine learning.
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    Identifying the most influential roads based on traffic correlation networks
    (Berlin ; Heidelberg [u.a.] : Springer Open, 2019) Guo, Shengmin; Zhou, Dong; Fan, Jingfang; Tong, Qingfeng; Zhu, Tongyu; Lv, Weifeng; Li, Daqing; Havlin, Shlomo
    Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows. © 2019, The Author(s).
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    Improved earthquake aftershocks forecasting model based on long-term memory
    ([London] : IOP, 2021) Zhang, Yongwen; Zhou, Dong; Fan, Jingfang; Marzocchi, Warner; Ashkenazy, Yosef; Havlin, Shlomo
    A prominent feature of earthquakes is their empirical laws, including memory (clustering) in time and space. Several earthquake forecasting models, such as the epidemic-type aftershock sequence (ETAS) model, were developed based on these empirical laws. Yet, a recent study [1] showed that the ETAS model fails to reproduce the significant long-term memory characteristics found in real earthquake catalogs. Here we modify and generalize the ETAS model to include short- and long-term triggering mechanisms, to account for the short- and long-time memory (exponents) discovered in the data. Our generalized ETAS model accurately reproduces the short- and long-term/distance memory observed in the Italian and Southern Californian earthquake catalogs. The revised ETAS model is also found to improve earthquake forecasting after large shocks.
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    Statistical physics approaches to the complex Earth system
    (Amsterdam [u.a.] : Elsevier Science, North-Holland, 2020) Fan, Jingfang; Meng, Jun; Ludescher, Josef; Chen, Xiaosong; Ashkenazy, Yosef; Kurths, Jürgen; Havlin, Shlomo; Schellnhuber, Hans Joachim
    Global warming, extreme climate events, earthquakes and their accompanying socioeconomic disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, multiple interactions and complex structures of the Earth system, the understanding and, in particular, the prediction of such disruptive events represent formidable challenges to both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge of the Earth system, including climate extreme events, earthquakes and geological relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as critical phenomena, network theory, percolation, tipping points analysis, and entropy can be applied to complex Earth systems. Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics concepts and theories can be useful in the field of Earth system science.