Predictive Resilience Modeling Using Statistical Regression Methods

Priscila Silva (), Mariana Hidalgo, Mindy Hotchkiss, Lasitha Dharmasena, Igor Linkov and Lance Fiondella ()
Additional contact information
Priscila Silva: Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA
Mariana Hidalgo: Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA
Mindy Hotchkiss: Aerojet Rocketdyne, Jupiter, FL 33478, USA
Lasitha Dharmasena: Faculty of Business and Law, Deakin University, Burwood, VIC 3125, Australia
Igor Linkov: Engineer Research and Development Center, U.S. Army Corps of Engineers, Concord, MA 01742, USA
Lance Fiondella: Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA

Mathematics, 2024, vol. 12, issue 15, 1-30

Abstract: Resilience describes the capacity of systems to react to, withstand, adjust to, and recover from disruptive events. Despite numerous metrics proposed to quantify resilience, few studies predict these metrics or the restoration time to nominal performance levels, and these studies often focus on a single domain. This paper introduces three methods to model system performance and resilience metrics, which are applicable to various engineering and social science domains. These models utilize reliability engineering techniques, including bathtub-shaped functions, mixture distributions, and regression analysis incorporating event intensity covariates. Historical U.S. job loss data during recessions are used to evaluate these approaches’ predictive accuracy. This study computes goodness-of-fit measures, confidence intervals, and resilience metrics. The results show that bathtub-shaped functions and mixture distributions accurately predict curves possessing V, U, L, and J shapes but struggle with W and K shapes involving multiple disruptions or sudden performance drops. In contrast, covariate-based models effectively track all curve types, including complex W and K shapes, like the successive shocks in the 1980 U.S. recession and the sharp decline in the 2020 U.S. recession. These models achieve a high predictive accuracy for future performance and resilience metrics, evidenced by the low sum of square errors and high adjusted coefficients of determination.

Keywords: resilience models; resilience metrics; predictive analytics; multiple shocks; restoration; system performance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/15/2380/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/15/2380/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:15:p:2380-:d:1446748

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().