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A disaster response model driven by spatial-temporal forecasts

Nikolopoulos, Konstantinos, Petropoulos, Fotios, Sanchez Rodrigues, Vasco ORCID:, Pettit, Stephen ORCID: and Beresford, Anthony ORCID: 2022. A disaster response model driven by spatial-temporal forecasts. International Journal of Forecasting 38 (3) , pp. 1214-1220. 10.1016/j.ijforecast.2020.01.002

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In this research, we propose a disaster response model combining preparedness and responsiveness strategies. The selective response depends on the level of accuracy that our forecasting models can achieve. In order to decide the right geographical space and time window of response, forecasts are prepared and assessed through a spatial–temporal aggregation framework, until we find the optimum level of aggregation. The research considers major earthquake data for the period 1985–2014. Building on the produced forecasts, we develop accordingly a disaster response model. The model is dynamic in nature, as it is updated every time a new event is added in the database. Any forecasting model can be optimized though the proposed spatial–temporal forecasting framework, and as such our results can be easily generalized. This is true for other forecasting methods and in other disaster response contexts.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Publisher: Elsevier
ISSN: 0169-2070
Date of First Compliant Deposit: 15 January 2020
Date of Acceptance: 6 January 2020
Last Modified: 07 Nov 2023 04:12

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