Wind models and cross-site interpolation for the refugee reception islands in Greece
This work addresses short-term forecasting of sea conditions for refugee influx patterns in Greece, but it is incremental as it applies existing statistical methods to a new dataset.
The study analyzed wind data from five Aegean Sea islands to predict wind speed using ARMA models, achieving a 1-day look-ahead RMSE of less than 1.9 km/h on average wind speed.
In this study, the wind data series from five locations in Aegean Sea islands, the most active `hotspots' in terms of refugee influx during the Oct/2015 - Jan/2016 period, are investigated. The analysis of the three-per-site data series includes standard statistical analysis and parametric distributions, auto-correlation analysis, cross-correlation analysis between the sites, as well as various ARMA models for estimating the feasibility and accuracy of such spatio-temporal linear regressors for predictive analytics. Strong correlations are detected across specific sites and appropriately trained ARMA(7,5) models achieve 1-day look-ahead error (RMSE) of less than 1.9 km/h on average wind speed. The results show that such data-driven statistical approaches are extremely useful in identifying unexpected and sometimes counter-intuitive associations between the available spatial data nodes, which is very important when designing corresponding models for short-term forecasting of sea condition, especially average wave height and direction, which is in fact what defines the associated weather risk of crossing these passages in refugee influx patterns.