Rain intensity forecast using Artificial Neural Networks in Athens, Greece - Nastos, P. T.; Moustris, K. P.; Larissi, I. K.; Paliatsos, A. G. (Atmospheric Research, 2013)
The forecast of extreme weather events become imperative due to the emerging climate change and possible adverse effects in humans. The objective of this study is to construct predictive models in order to forecast rain intensity (mm/day) in Athens, Greece, using Artificial Neural Networks (ANN) models. The ANNs outcomes concern the projected mean, maximum and minimum monthly rain intensity for the next four consecutive months in Athens. The meteorological data used to estimate the rain intensity, were the monthly rain totals (mm) and the respective rain days, which were acquired from the National Observatory of Athens, for a 111-year period (1899-2009). The results of the developed and applied ANN models showed a fairly reliable forecast of the rain intensity for the next four months. For the evaluation of the results and the ability of the developed prognostic models, appropriate statistical indices were taken into consideration. In general, the predicted rain intensity compared with the corresponding observed one seemed to be in a very good agreement at a statistical significance level of p. <. 0.01. © 2011 Elsevier B.V.
Keywords: Adverse effect; artificial neural network; Artificial neural networks; Athens; Athens , Greece; Athens [Attica]; Attica; Climate change; Extreme weather events; Forecasting; forecasting method; Greece; Infiltration; Meteorological data; Meteorology; Neural networks; precipitation intensity; Predictive models; Prognostic model; Rain days; Rain intensity; Rain-intensity; Statistical indices; statistical significance