Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/6240
Title: A novel framework for risk assessment and resilience of critical infrastructure towards climate change
Authors: Kumar, Nikhil
Poonia, Vikas
Goyal, Manish Kumar
Keywords: Critical infrastructures;Decision trees;Drought;Floods;Public works;Risk assessment;Co-occurrence;Extreme events;Extreme weather events;Industrial ecology;Long-term impacts;Precipitation extremes;Temporal evolution;Yield prediction;Climate change;climate change;computer simulation;infrastructural development;machine learning;numerical model;risk assessment;technological change;technological development
Issue Date: 2021
Publisher: Elsevier Inc.
Citation: Kumar, N., Poonia, V., Gupta, B. B., & Goyal, M. K. (2021). A novel framework for risk assessment and resilience of critical infrastructure towards climate change. Technological Forecasting and Social Change, 165 doi:10.1016/j.techfore.2020.120532
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. © 2020
URI: https://doi.org/10.1016/j.techfore.2020.120532
https://dspace.iiti.ac.in/handle/123456789/6240
ISSN: 0040-1625
Type of Material: Journal Article
Appears in Collections:Department of Civil Engineering

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