BROWSE

Related Researcher

Author's Photo

Kim, Pilwon
Nonlinear Complex Systems Lab
Research Interests
  • Complex systems, collective dynamics, nonlinear dynamical systems

ITEM VIEW & DOWNLOAD

Early warning for critical transitions using machine-based predictability

Cited 0 times inthomson ciCited 0 times inthomson ci
Title
Early warning for critical transitions using machine-based predictability
Author
Choi, JaesungKim, Pilwon
Issue Date
2022-09
Publisher
PO BOX 2604, SPRINGFIELD, USA, MO, 65801-2604
Citation
AIMS MATHEMATICS, v.7, no.11, pp.20313 - 20327
Abstract
Detecting critical transitions before they occur is challenging, especially for complex dynamical systems. While some early-warning indicators have been suggested to capture the phenomenon of slowing down in the system's response near critical transitions, their applicability to real systems is yet limited. In this paper, we propose the concept of predictability based on machine learning methods, which leads to an alternative early-warning indicator. The predictability metric takes a black-box approach and assesses the impact of uncertainties itself in identifying abrupt transitions in time series. We have applied the proposed metric to the time series generated from different systems, including an ecological model and an electric power system. We show that the predictability changes noticeably before critical transitions occur, while other general indicators such as variance and autocorrelation fail to make any notable signals.
URI
https://scholarworks.unist.ac.kr/handle/201301/59319
DOI
10.3934/math.20221112
ISSN
2473-6988
Appears in Collections:
MTH_Journal Papers
Files in This Item:
10.3934_math.20221112.pdf Download

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU