A disaster response model driven by spatial–temporal forecasts

Konstantinos Nikolopoulos, Fotios Petropoulos, Vasco Sanchez Rodrigues, Stephen Pettit and Anthony Beresford

International Journal of Forecasting, 2022, vol. 38, issue 3, 1214-1220

Abstract: 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.

Keywords: Disaster response; Forecasting; Spatial aggregation; Temporal aggregation; Earthquakes (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:3:p:1214-1220

DOI: 10.1016/j.ijforecast.2020.01.002

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