Reducing Risks in Agriculture

Nitu, Costica; Dumitrascu, Alexandru; Krapivin, Vladimir F.; Mkrtchyan, F.A.

doi:10.1109/cscs.2015.87

Cited by 18 publications

(1 citation statement)

References 2 publications

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“…Research has also been carried out for risk-based evacuation subzones (Hsu & Peeta, 2014) and risk reductions in different fields like agriculture (Iqbal, Ping, Abid, Kazmi, & Rizwan, 2016;Nitu, Dumitrascu, Krapivin, & Mkrtchyan, 2015), healthcare (Janius, Abdan, & Zulkaflli, 2016), and manufacturing enterprises (Pathak & Ahmad, 2016) in the riskmapped regions in different parts of the world.…”

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confidence: 99%

A multi‐objective framework for analysis of road network vulnerability for relief facility location during flood hazards: A case study of relief location analysis in Bankura District, India

Chakraborty

Das

Dasgupta

et al. 2018

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The location of disaster management facilities is a challenging and multifaceted problem. The road networks, population distribution along the road networks, and disaster risk maps are the major components for management of this problem. This article aims to decide on the location of such facilities for flood hazards in a region. The methodology is based on a multi‐objective framework. Objective functions include edge importance indices under fair weather and elevation parameters of the edges. Multiple scenarios are simulated for varying levels of hazard, and the outputs are analyzed. Analyses are carried out for the individual percentage loss of road links. A case study has been presented for the Bankura District in West Bengal, India. The inferences drawn from the results identify the critical links over the road networks of the region. The study also indicates locations in the region for relief facility setups to enable best‐serving capabilities and provide safe shelters, even in the most adverse flood conditions. The article depicts the vulnerability status of the road networks of the region. Further, it identifies the locations for relief facility provisioning that bring out the best road utilization and the best‐serving capabilities within the flood‐affected area under different flood levels.

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confidence: 99%