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Grey- and rough-set-based seasonal disaster predictions: an analysis of flood data in India
Published in Springer Netherlands
Volume: 97
Issue: 1
Pages: 395 - 435
Abstract: In a globally competitive market, companies attempt to foresee the occurrences of any catastrophe that may cause disruptions in their supply chains. Indian subcontinent is prone to frequent disasters related to floods and cyclones. It is essential for any supply chain operating in India to predict the occurrence of any such disasters. By doing so, the disaster management and the relief teams can prepare for the worst. This research makes use of a grey seasonal disaster prediction model to forecast the possible occurrence of any flood-related disasters in India. Flood data of major flood occurrences for a period of 10 years (2007–2017) have been taken for analysis in this context. We have established a grey model of the first order and with one variable, GM (1, 1), for prediction; from the results, we observe there are high chances of occurrence of a flood-related disaster in India during the early monsoon period (June–August), in both 2018 and 2020. By observing the prediction sequences on fatalities, there is likelihood that the death toll may rise above 100 and the flood can result in disastrous consequences. Also, the results of prediction are compared using an enhanced rough-set-based prediction model. From the results of rough-set-based prediction model, there are chances of a severe flood in mid-2018 in India. The results will be useful for organizations, NGOs and State Governments to carefully plan their supply and logistics network in the event of disasters. Graphic abstract: Proposed methodology of grey seasonal disaster prediction for floods.[Figure not available: see fulltext.]. © 2019, Springer Nature B.V.
About the journal
JournalData powered by TypesetNatural Hazards
PublisherData powered by TypesetSpringer Netherlands
Open AccessNo
Concepts (8)
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    Disaster management
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    Flood damage
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    Flood forecasting
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    Flood frequency
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    Seasonal variation
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