@article {4944,
	title = {Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece},
	journal = {Atmospheric Research},
	volume = {144},
	year = {2014},
	note = {Cited By :8Export Date: 2 November 2015Correspondence Address: Nastos, P.T.; Laboratory of Climatology and Atmospheric Environment, Department of Geography and Climatology, Faculty of Geology and Geoenvironment, University of Athens, Panepistimiopolis GR 157 84 Athens, Greece; email: nastos@geol.uoa.grReferences: Alpert, P., Ben-Gai, T., Baharad, A., Benjamini, Y., Yekutieli, D., Colacino, M., Diodato, L., Manes, A., The paradoxical increase of Mediterranean extreme daily rainfall in spite of decrease in total values (2002) Geophys. Res. Lett., 29 (11). , 31-1-31-4;Badjate, S.L., Dudul, S.V., Multi step ahead prediction of north and south hemisphere sun spots chaotic time series using focused time lagged recurrent neural network model (2009) WSEAS Trans. Inf. Sci. Appl., 6 (4), pp. 684-693; 
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},
	month = {2014},
	pages = {141 - 150},
	publisher = {Elsevier BV},
	abstract = {Extreme daily precipitation events are involved in significant environmental damages, even in life loss, because of causing adverse impacts, such as flash floods, in urban and sometimes in rural areas. Thus, long-term forecast of such events is of great importance for the preparation of local authorities in order to confront and mitigate the adverse consequences. The objective of this study is to estimate the possibility of forecasting the maximum daily precipitation for the next coming year. For this reason, appropriate prognostic models, such as Artificial Neural Networks (ANNs) were developed and applied. The data used for the analysis concern annual maximum daily precipitation totals, which have been recorded at the National Observatory of Athens (NOA), during the long term period 1891-2009. To evaluate the potential of daily extreme precipitation forecast by the applied ANNs, a different period for validation was considered than the one used for the ANNs training. Thus, the datasets of the period 1891-1980 were used as training datasets, while the datasets of the period 1981-2009 as validation datasets. Appropriate statistical indices, such as the coefficient of determination (R2), the index of agreement (IA), the Root Mean Square Error (RMSE) and the Mean Bias Error (MBE), were applied to test the reliability of the models. The findings of the analysis showed that, a quite satisfactory relationship (R2=0.482, IA=0.817, RMSE=16.4mm and MBE=+5.2mm) appears between the forecasted and the respective observed maximum daily precipitation totals one year ahead. The developed ANN seems to overestimate the maximum daily precipitation totals appeared in 1988 while underestimate the maximum in 1999, which could be attributed to the relatively low frequency of occurrence of these extreme events within GAA having impact on the optimum training of ANN. {\textcopyright} 2013 Elsevier B.V.},
	keywords = {artificial neural network, Artificial neural networks, Athens, Athens [Attica], Attica, Coefficient of determination, Daily precipitations, Environmental damage, extreme event, Extreme precipitation, forecasting method, Greece, Index of agreements, Maximum daily precipitation, Mean square error, Neural networks, numerical model, precipitation (climatology), Precipitation (meteorology), Root mean square errors, Rural areas, Total precipitation, weather forecasting},
	isbn = {01698095 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84899631944\&partnerID=40\&md5=04954fde179924a67dd3bbf24015d5b6},
	author = {Nastos, P. T. and Paliatsos, A. G. and Koukouletsos, K. V. and Larissi, I. K. and Moustris, K. P.}
}
