Self-Learning Cellular Automata for Forecasting Precipitation from Radar Images
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 2
Abstract
This paper presents a new forecasting methodology that uses self-learning cellular automata (SLCA) for including variables that consider the spatial dynamics of the mass of precipitation in a radar forecast model. Because the meteorological conditions involve nonlinear dynamic behavior, an automatic learning model is used to aid the cellular automata rules (SLCA). The new methodology is applied to the western part of England (Brue river basin) using NIMROD data. The radar information from 1 month of hourly radar measurements is used. Two models, a regression model tree (MT) and an artificial neural network (ANN) model, are used to learn the dynamics of the spatially local effects within the cellular automation (CA) neighboring areas. A spatial correlation (tracking pattern) reference model is built for comparing the first hour of precipitation forecast. Model results show that the SLCA is more accurate than conventional tracking. Furthermore, it appears that this technique can be extended to include other important atmospheric variables in forecasting processes.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors greatly acknowledge the data supplier: the British Atmospheric Data Centre.
References
Almeida, C. M., Gleriani, J. M., Castejon, E. F., and Soares, B. S. (2008). “Using neural networks and cellular automata for modelling intra-urban land-use dynamics.” Int. J. Geogr. Inf. Sci., 22(9), 943–963.
Balzter, H., Braun, P. W., and Kohler, W. (1998). “Cellular automata models for vegetation dynamics.” Ecol. Model., 107(2–3), 113–125.
Cluckie, I. D., Rico-Ramirez, M. A., and Xuan, Y. (2006). “An experiment of rainfall prediction over the Odra catchment by combining weather radar and numerical weather model.” Proc., 7th Int. Conf. on Hydroinformatics HIC 2006, Nice, France.
Corzo, G., Xuan, Y., Martinez, C. A., and Solomatine, D. (2009). “Ensemble of radar and MM5 precipitation forecast models with M5 model trees.” IAHS Redbook of Proc. of Symp. JS.4 at the Joint Convention of the Int. Association of Hydrological Sciences (IAHS) and Int. Association of Hydrogeologists (IAH), IAHS Publication 331, IAHS, Wallingford, UK.
Elshorbagy, A., Corzo, G., Srinivasulu, S., and Solomatine, D. P. (2010). “Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology—Part 1: Concepts and methodology.” Hydrol. Earth Syst. Sci., 14(10), 1931–1941.
Golding, B. (1998). “Nimrod: A system for generating automated very short range forecast.” Meteorol. Appl., 5(1), 1–16.
Levenberg, K. (1944). “A method for the solution of certain problems in least squares.” Q. Appl. Math., 2, 164–168.
Li, H. (2009). “Spatial pattern dynamics in aquatic ecosystem modelling.” Ph.D. thesis, CRC Press/Balkema, Leiden, the Netherlands.
Li, H., Arias, M., Blauw, A., Los, H., Mynett, A. E., and Peters, S. (2010). “Enhancing generic ecological model for short-term prediction of southern North Sea algal dynamics with remote sensing images.” J. Ecol. Model., 221(20), 2435–2446.
Li, H., Mynett, A. E., and Corzo, G. (2007). “Model-based training of artificial neural networks and cellular automata for rapid prediction of potential algae blooms.” Proc., 6th Int. Symp. on Ecohydraulics, Christchurch, New Zealand.
Li, Z., Ge, W., Liu, J., and Zhao, K. (2004). “Coupling between weather radar rainfall data and a distributed hydrological model for real-time flood forecasting.” Hydrol. Sci. J., 49(6), 945–958.
Marquardt, D. (1963). “An algorithm for least-squares estimation of nonlinear parameters.” SIAM J. Appl. Math., 11(2), 431–441.
MATLAB version 7.11.0584 [Computer software]. (2010). MathWorks, Natick, MA.
Packard, N. H., and Wolfram, S. (1985). “Two-dimensional cellular automata.” J. Stat. Phys., 38, 901–946.
Pessoa, M., Bras, R., and Williams, E. (1993). “Use of weather radar for flood forecasting in the Sieve river basin: A sensitivity analysis.” J. Appl. Meteorol., 32(3), 462–475.
Quinlan, J. R. (1986). “Induction of decision trees.” Mach. Learn., 1(1), 181–186.
Quinlan, J. R. (1992). “Learning with continuous classes.” Proc. of Australian Joint Conf. on AI, World Scientific, Singapore, 343–348.
Schaefer, J. T. (1990). “The critical success index as an indicator of warning skill.” Weather Forecasting, 5(4), 570–575.
Stanski, H. R., Wilson, L. J., and Burrows, W. R. (1989). “Survey of common verification methods in meteorology.”, Atmospheric Environment Service, Forecast Research Division, Geneva.
Sun, X., Mein, R. G., Keenan, T. D., and Elliot, J. F. (2000). “Flood estimation using radar and rain gauge data.” J. Hydrol. (Amsterdam), 239(1–4), 4–18.
Werner, M., Blazkova, S., and Petr, J. (2005). “Spatially distributed observations in constraining inundation modelling uncertainties.” Hydrol. Processes, 19(16), 3081–3096.
Witten, I., and Frank, E. (2005). Data mining: Practical machine learning tools and techniques, 2nd Ed., Morgan Kaufmann, San Francisco.
Wolfram, S. (1986). Theory and applications of cellular automata: Including selected papers 1983–1986, World Scientific, Singapore.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 18 • Issue 2 • February 2013
Pages: 206 - 211
Copyright
© 2013 American Society of Civil Engineers.
History
Received: May 12, 2011
Accepted: Apr 30, 2012
Published online: Jan 15, 2013
Published in print: Feb 1, 2013
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.