@article {4965,
	title = {Rain intensity forecast using Artificial Neural Networks in Athens, Greece},
	journal = {Atmospheric Research},
	volume = {119},
	year = {2013},
	note = {Cited By :10Export Date: 2 November 2015Correspondence Address: Nastos, P.T.; Laboratory of Climatology and Atmospheric Environment, Faculty of Geology and Geoenvironment, University of Athens, Panepistimiopolis, GR 157 84, Athens, Greece; email: nastos@geol.uoa.grReferences: Bielec-Bakowska, S., Lupikasza, E., Long-term precipitation variability on thunderstorm days in Poland (1951-2000) (2009) Atmos. Res., 93, pp. 506-515;Bodri, L., Cermak, V., Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia (2000) Adv. Eng. Softw., 31, pp. 211-221; 
Caudill, M., Butler, C., (1992) Understanding Neural Networks; Computer Explorations, p. 310. , MIT Press, Cambridge, MA, USA; 
Cigizoglou, H.K., Alp, M., Rainfall-runoff modelling using three neural network methods (2004) Artificial Intelligence and Soft Computing-ICAISC 2004 Lecture Notes in artificial Intelligence, 3070, pp. 166-171; 
Comrie, A.C., Comparing neural networks and regression models for ozone forecasting (1997) J. Air Waste Manage., 47, pp. 653-663; 
Diamantopoulou, M.J., Georgiou, P.E., Papamichail, D.M., Performance of neural network models with Kalman learning rule for flow routing in a river system (2007) Fresenius Environ. Bull., 16 (11 B), pp. 1474-1484; 
Federico, S., Avolio, E., Pasqualoni, L., De Leo, L., Sempreviva, A.M., Bellecci, C., Preliminary results of a 30-year daily rainfall data base in southern Italy (2009) Atmos. Res., 94, pp. 641-651; 
Freiwan, M., Cigizoglu, H.K., Prediction of total monthly rainfall in Jordan using feed forward back propagation method (2005) Fresenius Environ. Bull., 14 (2), pp. 142-151; 
French, M.N., Krajewski, W.F., Cuykendall, R.R., Rainfall forecasting in space and time using neural network (1992) J. Hydrol., 137, pp. 1-31; 
Gwangseob, K., Ana, P.B., Quantitative flood forecasting using multisensor data and neural networks (2001) J. Hydrol., 246, pp. 45-62; 
Hatzaki, M., Flocas, H.A., Oikonomou, C., Giannakopoulos, C., Future changes in the relationship of precipitation intensity in Eastern Mediterranean with large scale circulation (2010) Adv. Geosci., 23, pp. 31-36; 
Hecht-Nielsen, R., Theory of back propagation neural network (1989) Proceedings of the International Joint Conference on Neural Networks, New York, IEEE, pp. 593-605; 
Hopfield, J.J., Neural networks and physical systems with emergent collective computational abilities (1982) PNAS, 9, pp. 2554-2558; 
Hopfield, J.J., Learning algorithms and probability distributions in feed-forward and feed-back networks (1987) PNAS, 84, pp. 8429-8433; 
Summary for policymakers (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, , Cambridge University Press, Cambridge, United Kingdom/New York, NY, USA, S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, H.L. Miller (Eds.), IPCC; 
Iseri, Y., Dandy, G.C., Maier, H.R., Kawamura, A., Jinno, K., Medium term forecasting of rainfall using artificial neural networks (2005) Proceedings of the International Congress on Modeling and Simulation. Advances and Applications for Management and Decision Making, pp. 1834-1840. , The Modeling and Simulation Society of Australia and New Zealand Inc, Melbourne, Australia; 
Kolehmainen, M., Martikainen, H., Ruuskanen, J., Neural networks and periodic components used in air quality forecasting (2001) Atmos. Environ., 35, pp. 815-825; 
Koukis, G.C., Koutsoyiannis, D., (1997) Geomorphological Hazards of Europe, pp. 215-241. , Greece, Elsevier, Amsterdam, C.A.C. Embleton (Ed.); 
Luck, K.C., Ball, J.E., Sharma, A., A study of optimal model lag and spatial inputs to artificial neural networks for rainfall forecasting (2000) J. Hydrol., 227 (1-4), pp. 56-65; 
Manzatο, Α., Sounding-derived indices for neural network based short-term thunderstorm and rainfall forecasts (2007) Atmos. Res., 83, pp. 349-365; 
Mar, K.W., Naing, T.T., Optimum neural network architecture for precipitation prediction of Myanmmar (2008) World Acad. Sci. Eng. Technol., 38, pp. 130-134; 
Marzano, F.S., Fionda, E., Ciotti, P., Neural-network approach to ground-based passive microwave estimation of precipitation intensity and extinction (2006) J. Hydrol., 328, pp. 121-131; 
Mateo, J., Ballart, D., Brucet, C., Aran, M., Bech, J., A study of a heavy rainfall event and a tornado outbreak during the passage of a squall line over Catalonia (2009) Atmos. Res., 93, pp. 131-146; 
McCulloch, W., Pitts, W., A logical calculus of the ideas imminent in nervous activity (1943) Bull. Math. Biophys., 5, pp. 33-115; 
Melas, D., Kioutsoukis, I., Ziomas, I., Neural network model for predicting peak photochemical pollutant levels (2000) J. Air Waste Manage., 50, pp. 495-501; 
Metaxas, D.A., Philandras, C.M., Nastos, P.T., Repapis, C.C., Variability of precipitation pattern in Greece during the year (1999) Fresenius Environ. Bull., 8 (1-2), pp. 1-6; 
Michaelides, S.C., Pattichis, C.S., Kleovoulou, G., Classification of rainfall variability by using artificial neural networks (2001) Int. J. Climatol., 21, pp. 1401-1414; 
Moustris, K., Ziomas, I.C., Paliatsos, A.G., 24 hours in advance forecasting of thermal comfort-discomfort levels during the hot period of the year at representative locations of Athens city, Greece (2009) Fresenius Environ. Bull., 18 (5), pp. 601-608; 
Moustris, K.P., Ziomas, I.C., Paliatsos, A.G., 3-day-ahead forecasting of regional pollution index for the pollutants NO2, CO, SO2 and O3 using Artificial Neural Networks in Athens, Greece (2010) Water Air Soil Pollut., 209, pp. 29-43; 
Nastos, P.T., Spatial and temporal variability of rain intensity in Greece (2008) Proceedings of 8th International Hydrogeological Congress, Athens, pp. 651-660; 
Nastos, P.T., Matsangouras, J.T., Tornado activity in Greece within the 20th century (2010) Adv. Geosci., 26, pp. 49-51; 
Nastos, P.T., Zerefos, C.S., On extreme daily precipitation totals at Athens, Greece (2007) Adv. Geosci., 10, pp. 59-66; 
Nastos, P.T., Zerefos, C.S., Decadal changes in extreme daily precipitation in Greece (2008) Adv. Geosci., 16, pp. 55-62; 
Nastos, P.T., Zerefos, C.S., Spatial and temporal variability of consecutive dry and wet days in Greece (2009) Atmos. Res., 94, pp. 616-628; 
Orlandini, S., Morlini, I., Artificial neural network estimation of rainfall intensity from radar observations (2000) J. Geophys. Res., 105 (D20), pp. 24849-24861; 
Paliatsos, A.G., Nastos, P.T., Tzavelas, G., Panagiotakos, D.B., Characteristics of precipitation in Athens area, Greece (2005) Fresenius Environ. Bull., 14 (5), pp. 422-428; 
Papanastasiou, D.K., Melas, D., Kioutsioukis, I., Development and assessment of neural network and multiple regression models in order to predict PM10 levels in a medium-sized Mediterranean city (2007) Water Air Soil Pollut., 182, pp. 325-334; 
Philandras, C.M., Nastos, P.T., Paliatsos, A.G., Repapis, C.C., Study of the rain intensity in Athens and Thessaloniki, Greece (2010) Adv. Geosci., 23, pp. 37-45; 
Philandras, C.M., Douvis, K.C., Nastos, P.T., Tselioudis, G.B., Zerefos, C.S., Study of the precipitation trends in the Mediterranean region (2010) Proceedings of the 10th Conference on Climatology, Meteorology and Atmospheric Physics (COMECAP2010), 25-28/5/2010, Patras, Greece, pp. 785-792. , (in Greek); 
Queralt, S., Hern{\'a}ndez, E., Barriopedro, D., Gallego, D., Ribera, P., Casanova, C., North Atlantic Oscillation influence and weather types associated with winter total and extreme precipitation events in Spain (2009) Atmos. Res., 94, pp. 675-683; 
Rosenblatt, F., The Perceptron: a probabilistic model for information storage and organization in the brain (1958) Psychol. Rev., 65 (6), pp. 386-408; 
Sahai, A.K., Soman, M.K., Satyan, V., All India summer monsoon rainfall prediction using an artificial neural network (2000) Clim. Dyn., 16, pp. 291-302; 
Sakellariou, N.K., Kambezidis, H.D., Prediction of the total rainfall amount during August and November in the Athens area, Greece (2004) Fresenius Environ. Bull., 13 (3), pp. 289-292; 
Sengorur, B., Dogan, E., Koklu, R., Samandar, A., Dissolved oxygen estimation using artificial neural network for water quality control (2006) Fresenius Environ. Bull., 15 (9), pp. 1064-1067; 
Silverman, D., Dracup, J.A., Artificial neural networks and long-range precipitation predictions in California (2000) J. Appl. Meteorol., 39 (1), pp. 57-66; 
Sokol, Z., Bli{\v z}{\v n}{\'a}k, V., Areal distribution and precipitation-altitude relationship of heavy short-term precipitation in the Czech Republic in the warm part of the year (2009) Atmos. Res., 94, pp. 652-662; 
Solomon, S., Plattner, G.-K., Knutti, R., Friedlingstein, P., Irreversible climate change due to carbon dioxide emissions (2009) PNAS, 106 (6), pp. 1704-1709; 
Tzavelas, G., Paliatsos, A.G., Nastos, P.T., Models for exceedances over high thresholds of the precipitation totals over Athens (2010) Greece. Nat. Hazard Earth Syst., 10, pp. 105-108; 
Walker, S.E., Slordal, H.L., Guerreiro, C., Gram, F., Gronskei, E.K., Air pollution exposure monitoring and estimation Part II. Model evaluation and population exposure (1999) J. Environ. Monit., 1, pp. 321-326; 
Wardah, T., Abu Bakar, S.H., Bardossy, A., Maznorizan, M., Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting (2008) J. Hydrol., 356, pp. 283-298; 
Werbos, P., Beyond regression: new tools for prediction and analysis in the behavioural sciences (1974), Ph.D. dissertation, Committee on Applied Mathematics, Harvard University, Cambridge, MAWillmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., O{\textquoteright}Donnell, J., Rowe, C., Statistics for the evaluation and comparison of models (1985) J. Geophys. Res., 90, pp. 8995-9005; 
Wilmott, C.J., Some comments on the evaluation of model performance (1982) Bull. Am. Meteorol. Soc., 63 (11), pp. 1309-1313; 
Zwick, A., Canarelli, P., Climate sensitivity analysis using neural networks (1996) Fresenius Environ. Bull., 5 (11-12), pp. 700-705; 
Zwiers, F.W., Von Storch, H., On the role of statistics in climate research (2004) Int. J. Climatol., 24, pp. 665-680
},
	month = {2013},
	pages = {153 - 160},
	abstract = {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. {\textcopyright} 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},
	isbn = {01698095 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870245889\&partnerID=40\&md5=099a4227d94c4b09bebe2e89fabf8685},
	author = {Nastos, P. T. and Moustris, K. P. and Larissi, I. K. and Paliatsos, A. G.}
}
