Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece

Citation:

Nastos, P.T., Paliatsos, A.G., Koukouletsos, K.V., Larissi, I.K. & Moustris, K.P. Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece. Atmospheric Research 144, 141 - 150 (2014). Copy at http://www.tinyurl.com/y4veczh9

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. © 2013 Elsevier B.V.

Notes:

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; Barlow, M., Influence of hurricane-related activity on North American extreme precipitation (2011) Geophys. Res. Lett., 38, pp. L04705. , (5 pp.); Bartzokas, A., Lolis, C.J., Metaxas, D.A., The 850hPa relative vorticity centres of action for winter precipitation in the Greek area (2003) Int. J. Climatol., 23, pp. 813-828; Beniston, M., Stephenson, D.B., Christensen, O.B., Ferro, C.A.T., Frei, C., Goyette, S., Halsnaes, K., Woth, K., Future extreme events in European climate: an exploration of regional climate model projections (2007) Clim. Chang., 81, pp. 71-95; 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; Bornstein, R., Lin, Q., Urban heat islands and summertime convective thunderstorms in Atlanta: three cases studies (2000) Atmos. Environ., 34, pp. 507-516; Brunetti, M., Maugeri, M., Nanni, T., Changes in total precipitation, rainy days and extreme events in northeastern Italy (2001) Int. J. Climatol., 21, pp. 861-871; Caudill, M., Butler, C., (1922) Understanding Neural Networks, , MIT Press; Cicek, I., Turkoglu, N., Urban effects on precipitation in Ankara (2005) Atmoosfera, 18 (3), pp. 173-187; 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; Diodato, N., Bellocchi, G., Drought stress patterns in Italy using agro-climatic indicators (2008) Clim. Res., 36 (1), pp. 53-63; Easterling, D.R., Evans, J.L., Groisman, P., Karl, T.R., Kunkel, K.E., Ambenje, P., Observed variability and trends in extreme climate events: a brief review (2000) Bull. Am. Meteorol. Soc., 81, pp. 417-425; 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; Furrer, E.M., Katz, R.W., Improving the simulation of extreme precipitation events by stochastic weather generators (2008) Water Resour. Res., 44, pp. W12439; Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, , Addison-Wesley Pub. Co; Goldreich, Y., Manes, A., Urban effects on precipitation patterns in the greater Tel-Aviv area (1979) Archiv für Meteorologie, Geophysik und Bioklimatologie, Serie B, 27 (2-3), pp. 213-224; Goswami, P., Shivappa, H., Goud, B.S., Impact of urbanization on tropical mesoscale events: investigation of three heavy rainfall events (2010) Meteorol. Z., 19 (4), pp. 385-397; Groisman, P., Knight, R.W., Easterling, D.R., Karl, T.R., Hegerl, G.C., Trends in intense precipitation in the climate record (2005) J. Clim., 18 (9), pp. 1326-1350; Gunhan, T., Demir, V., Hancioglu, E., Hepbasli, A., Mathematical modelling of drying of bay leaves (2005) Energy Convers. Manag., 46, pp. 1667-1679; Guo, X., Fu, D., Wang, J., Mesoscale convective precipitation system modifiedd by urbanization in Beijing City (2006) Atmos. Res., 82, pp. 112-126; Hect-Nielsen, R., (1990) Neurocomputing, , Addison-Wesley, Reading, M.A; (2001) Climate Change 2001: The Scientific Basis. Contribution of Working Group 1 to the Third IPCC Scientific Assessment, Chapter 8, Model Evaluation, , IPCC; 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; Iwashima, T., Yamamoto, R., A statistical analysis of the extreme events: long-term trend of heavy daily precipitation (1993) J. Meteorol. Soc. Jpn., 71, pp. 637-640; Jauregui, E., Romales, E., Urban effects on convective precipitation in Mexico City (1996) Atmos. Environ., 30 (20), pp. 3383-3389; Jolliffe, I.T., (1986) Principal Component Analysis, , Springer-Verlag, New York; Kale, S.N., Dudul, S.V., Intelligent noise removal from EMG signal using focused time-lagged recurrent neural network (2009) Applied Computational Intelligence and Soft Computing Article ID 129761, p. 12; Kambezidis, H.D., Larissi, I.K., Nastos, P.T., Paliatsos, A.G., Spatial variability and trends of the rain intensity over Greece (2010) Adv. Geosci., 26, pp. 65-69; Kamp, R.G., Savenije, H.H.G., Optimising training data for ANNs with genetic algorithms (2006) Hydrol. Earth Syst. Sci. Discuss., 3, pp. 285-297; Karl, T.R., Knight, R.W., Secular trends of precipitation amount frequency and intensity in the United States (1998) Bull. Am. Meteorol. Soc., 79, pp. 231-241; Karl, T.R., Knight, R.W., Plummer, N., Trends in high-frequency climate variability in the twentieth century (1995) Nature, 377, pp. 217-220; Kolehmainen, M., Martikainen, H., Ruuskanen, J., Neural networks and periodic components used in air quality forecasting (2001) Atmos. Environ., 35, pp. 815-825; Kostopoulou, E., Jones, P.D., Assessment of climate extremes in the Eastern Mediterranean (2005) Meteorol. Atmos. Phys., 89, pp. 69-85; Koutsoyiannis, D., Statistics of extremes and estimation of extreme rainfall: I. Theoretical investigation (2004) Hydrol. Sci. J., 49 (4), pp. 575-590; Krause, P., Boyle, D.P., Bäsel, F., Comparison of different efficiency criteria for hydrological model assessment (2005) Adv. Geosci., 5, pp. 89-97; Kundzewicz, Z.W., Radziejewski, M., Pinskwar, I., Precipitation extremes in the changing climate of Europe (2006) Clim. Res., 31 (1), pp. 51-58; Lehner, B., Döll, P., Alcamo, J., Henrichs, T., Kaspar, F., Estimating the impact of global change on flood and drought risks in Europe: a continental, integrated analysis (2006) Clim. Chang., 75 (3), pp. 273-299; 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; Manly, B.F.J., (1986) Multivatiate Statistical Methods: A Primer, , Chapman & Hall, London; Manzato, A., 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 Myanmar (2008) World Acad. Sci. Eng. Technol., 48, 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; Min, S.-K., Zhang, X., Zwiers, F.W., Hegerl, G.C., Human contribution to more-intense precipitation extremes (2011) Nature, 470, pp. 378-381; 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; Moustris, K.P., Larissi, I.K., Nastos, P.T., Paliatsos, A.G., Precipitation forecast using artificial neural networks in specific regions of Greece (2011) Water Resour. Manag., 25, pp. 1979-1993; 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; Nastos, P.T., Philandras, C.M., Repapis, C.C., Application of canonical analysis to air temperature and precipitation regimes over Greece (2002) Fresenius Environ. Bull., 11 (8), pp. 488-493; Paliatsos, A.G., Nastos, P.T., Tzavelas, G., Panagiotakos, D.B., Characteristics of precipitation in urban Athens area, from 1891 to 2000 (2005) Fresenius Environ. Bull., 14, pp. 422-428; Pall, P., Aina, T., Stone, D.A., Stott, P.A., Nozawa, T., Hilberts, A.G.J., Lohmann, D., Allen, M.R., Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000 (2011) Nature, 470, pp. 382-385; Philandras, C.M., Metaxas, D.A., Nastos, P.T., Climate variability and urbanization in Athens (1999) Theor. Appl. Climatol., 63 (1-2), pp. 65-72; 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; Richman, M.B., Rotation of principal components (1986) J. Climatol., 6, pp. 293-335; Rumelhart, D.E., Hinton, G.E., Williams, R.J., (1986) Parallel Distributed Processing Explorations in the Microstructure of Cognition, 1, pp. 318-362. , MIT Press, Bradfords Books, Cambridge, MA, D.E. Rumelhart, J.L. McClelland (Eds.); 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; Sheffield, J., Wood, E.F., Characteristics of global and regional drought, 1950-2000: analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle (2007) J. Geophys. Res. D Atmos., 112 (17). , (No D17115); Silverman, D., Dracup, J.A., Artificial neural networks and long-range precipitation predictions in California (2000) J. Appl. Meteorol., 39 (1), pp. 57-66; Suppiah, R., Hennessy, K.J., Trends in total rainfall events and number of dry events in Australia. 1910-1990 (1998) Int. J. Climatol., 18, pp. 1141-1164; Walker, S.-E., Slordal, H.L., Guerreiro, C., Gram, F., Grønskei, E.K., Air pollution exposure monitoring and estimation Part II. Model evaluation and population exposure (1999) J. Environ. Monit., 1, pp. 321-326; Wang, Y.-M., Traore, S., Time-lagged recurrent network for forecasting episodic event suspended sediment load in typhoon prone area (2009) Int. J. Phys. Sci., 4 (9), pp. 519-528; 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; Willmott, C.J., Some comments on the evaluation of model performance (1982) Bull. Am. Meteorol. Soc., 63 (11), pp. 1309-1313; Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., O'Donnell, J., Rowe, C., Statistics for the evaluation and comparison of models (1985) J. Geophys. Res., 90, pp. 8995-9005; Xue, Y., Dibike, Y.B., Flood forecasting mode for Huai River in China using time delay neural network (2001) Proceedings of the XXIX IAHR congress, Beijing, China, Theme C, pp. 59-66; Yonetani, T., Increase in Number of Days with Heavy Precipitation in Tokyo Urban Area (1982) J. Appl. Meteor., 21, pp. 1466-1471. , <1466:IINODW>2.0.CO;2

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