A novel framework for risk assessment and resilience of critical infrastructure towards climate change

Nikhil Kumar, Vikas Poonia, B.B. Gupta and Manish Kumar Goyal

Technological Forecasting and Social Change, 2021, vol. 165, issue C

Abstract: The persistent extreme weather events (floods, droughts, heatwaves, etc.) are increasing the risks towards critical infrastructure (C.I). Therefore, it is essential to enhance the resilience of our C.I to withstand such events in the present and future. Here, a review of current and projected impacts of climate change is conducted on extreme events and on possible implications on C.I is carried out, which suggests that such events can have a severe impact on C.Is. Also, two studies on the behaviour of precipitation extremes and temporal evolution of drought across India are carried out, taking into account the corresponding impacts on C.Is. It indicated that north-western, north-eastern westernmost regions and western Ghats are highly susceptible to floods and northern, central-eastern, western, and central regions are prone towards co-occurrence of floods and droughts. Also, a case study on Kharif paddy yield forecasting using different machine learning (ML) models is carried out, where the random forest was found to be the most suitable model for yield prediction. Finally, we put forward a robust framework for risk assessment and improving the resilience of C.Is based upon the principles of flexibility, diversity, and industrial ecology, incorporating both short-term and long-term impacts of climate risk.

Keywords: Risk; Climate change; Machine learning; Critical infrastructure (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:165:y:2021:i:c:s0040162520313585

DOI: 10.1016/j.techfore.2020.120532

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