Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 13 9 2009 10.5194/hess-13-1607-2009 http://www.hydrol-earth-syst-sci.net/13/1607/2009/ http://www.hydrol-earth-syst-sci.net/13/1607/2009/hess-13-1607-2009.html http://www.hydrol-earth-syst-sci.net/13/1607/2009/hess-13-1607-2009.pdf 1607 1618 2009-09-10 River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin M. K. Akhtar G. A. Corzo corzogac@yahoo.es S. J. van Andel A. Jonoski UNESCO-IHE Institute for Water Education, Dept. of Hydroinformatics and Knowledge management, P.O. Box 3015, 2601 Delft, The Netherlands University of western Ontario, Dept. of Civil and Environmental Engineering, Spencer Engineering Building, London, Ontario, N6A 5B9, Canada This paper explores the use of flow length and travel time as a pre-processing step for incorporating spatial precipitation information into Artificial Neural Network (ANN) models used for river flow forecasting. Spatially distributed precipitation is commonly required when modelling large basins, and it is usually incorporated in distributed physically-based hydrological modelling approaches. However, these modelling approaches are recognised to be quite complex and expensive, especially due to the data collection of multiple inputs and parameters, which vary in space and time. On the other hand, ANN models for flow forecasting are frequently developed only with precipitation and discharge as inputs, usually without taking into consideration the spatial variability of precipitation. Full inclusion of spatially distributed inputs into ANN models still leads to a complex computational process that may not give acceptable results. Therefore, here we present an analysis of the flow length and travel time as a basis for pre-processing remotely sensed (satellite) rainfall data. This pre-processed rainfall is used together with local stream flow measurements of previous days as input to ANN models. The case study for this modelling approach is the Ganges river basin. A comparative analysis of multiple ANN models with different hydrological pre-processing is presented. The ANN showed its ability to forecast discharges 3-days ahead with an acceptable accuracy. Within this forecast horizon, the influence of the pre-processed rainfall is marginal, because of dominant influence of strongly auto-correlated discharge inputs. For forecast horizons of 7 to 10 days, the influence of the pre-processed rainfall is noticeable, although the overall model performance deteriorates. The incorporation of remote sensing data of spatially distributed precipitation information as pre-processing step showed to be a promising alternative for the setting-up of ANN models for river flow forecasting. Abrahart, R J. and See, L.: Comparing neural network and autoregressive moving average techniques for the provision of continous river flow forecasts in two contrasting catchments, Hydrol. Process., 14, 2157–2172, 2000. Abrahart, R J., Heppenstall, A J., and See, L M.: Timing error correction procedure applied to neural network rainfall-runoff modelling, Hydrolog. Sci. J., 52, 414–431, 2007. % % %Akhtar, M.: Flood Forecasting for Bangladesh with Satellite Data, UNESCO-IHE, % Master of Science, \blackbox\textbfpages?, 2006a. Akhtar, M K.: Flood Forecasting for Bangladesh with satellite Data, Msc Thesis, UNESCO-IHE, Delft, the Netherlands, 134~pp., 2006. ASCE: Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial Neural Networks in Hydrology, II:Hydrologic Application, J. Hydrol. Eng., 5, 124–136, 2000a. ASCE: Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial Neural Networks in Hydrology. I: Preliminary Concepts, J. Hydrol. Eng., 5, 115–123, 2000b. Bowden, G J., Dandy, G C., and Maier, H R.: Input determination for neural network models in water resources applications. Part 1-background and methodology, J. Hydrol., 301, 75–92, 2005a. Bowden, G J., Dandy, G C., and Maier, H R.: Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river, J. Hydrol., 301, 93–107, 2005b. Brath, A. and Rosso, R.: Adaptive calibration of a conceptual model for flash flood forecasting, Water Resour. Res., 29, 2561–2572, 1993. Brath, A., Montanari, A., and Toth, E.: Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models, Hydrol. Earth Syst. Sci., 6, 627–639, 2002. Campolo, M., Soldati, A., and Andreussi, P.: Artificial neural network approach to flood forecasting in the River Arno/Une approche à base de réseau de neurones artificiels pour la prévision des crues du fleuve Arno, Hydrolog. Sci. J., 48, 381–398, 2003. Chowdhury, M.: An assessment of flood forecasting in Bangladesh: the experience of the 1998 flood, Nat. Hazards, 22, 139–163, 2000. Corzo, G. and Solomatine, D.: Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting, Neural Networks, 20, 528–536, 2007a. Corzo, G., Solomatine, D., Hidayat, de Wit, M., Werner, M., Uhlenbrook, S., and Price, R.: Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin, Hydrol. Earth Syst. Sci. Discuss., 6, 729–766, 2009. Corzo, G A. and Solomatine, D P.: Baseflow separation techniques for modular artificial neural networks modelling in flow forecasting, Hydrolog. Sci. J., 52, 491–507, 2007b. Dawson, C., See, L., Abrahart, R., and Heppenstall, A.: Symbiotic adaptive neuro-evolution applied to rainfall–runoff modelling in northern England, Neural Networks, 19, 236–247, 2006. Elshorbagy, A., Corzo, G., Srinivasulu, S., and Solomatine, D.: Experimental investigation of the predictive capabilities of soft computing techniques in hydrology., Technical Rep., 49~pp., 2009. FFWC: Flood Forecasting and Warning Centre; Annual Flood Report 2007, Technical Rep., Dhaka, Bangladesh, 4, 86~pp., 2007. Huffman, G., Adler, R., Curtis, S., Bolvin, D., and Nelkin, E.: Global rainfall analyses at monthly and 3-hr time scales, Measuring Precipitation from Space: EURAINSAT and the Future, Springer, Dordrecht, The Netherlands, 28, 291–305, 2007. Jakobsen, F., Hoque, A. K. M. Z. , Paudyal, G. N., and Bhuiyan, S.: Evaluation of the Short-Term Processes Forcing the Monsoon River Floods in Bangladesh, Hydrolog. Sci. J., 30, 389–399, 2005. Kitanidis, P K. and Bras, R L.: Real-Time Forecasting With a Conceptual Hydrologic Model: Analysis of Uncertainty, Water Resour. Res., 16, 1025–1033, 1980. Lauzon, N., Anctil, F., and Baxter, C. W.: Clustering of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts, Hydrol. Earth Syst. Sci., 10, 485–494, 2006. Levenberg, K.: A method for the solution of certain problems in least squares, Quart. Appl. Math, 2, 164–168, 1944. Lin, J., Cheng, C., and Chau, K.: Using support vector machines for long-term discharge prediction, Hydrolog. Sci. J., 51, 599–612, 2006. Minns, A W. and Hall, M.: Artificial Neural Networks as rainfall-runoff models, Hydrolog. Sci. J., 41, 399–417, 1996. Mirza, M.: The runoff sensitivity of the Ganges river basin to climate change and its implications, J. Environ. Hydrol., 5, 1–13, 1997. Mirza, M.: Global warming and changes in the probability of occurrence of floods in Bangladesh and implications, Global Environmental Change, 12, 127–138, 2002. Moglen, G. E. and Maidment, D. R.: Digital Elevation Model Analysis and Geographic Information Systems, Encyclopedia of Hydrological Sciences, Part 2., Hydroinformatics, Vol 1, John Wiley & Sons, Ltd., Chichester, England, UK, 239–255, 2005. NASDA: TRMM data users Handbook, Technical Rep., 226~pp., 2001. Nash, J E. and Sutcliffe, J V.: River flow forecasting through conceptual models Part 1- A Discussion Principles, J. Hydrol., 10, 282–290, 1970. Toth, E.: Data-Driven Streamflow Simulation: The Influence of Exogenous Variables and Temporal Resolution, in: Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications, edited by: Abrahart, R. J., Linda, M. S., and Dimitri, P., Solomatine, Berlin Heidelberg, Germany, 2008. van Griensven, A., Akhtar, M. K., A., Haguma, D., Sintayehu, R., Schuol, J., Abbaspour, K., van Andel, S., and Price, R.: CATCHMENT Modeling using Internet-Based Global Data, 4th SWAT conference UNESCO-IHE Delft, The Netherlands, 2007. Wanielista, M. P., Kersten, E., and Robert, R.: Hydrology: Water Quantity and Quality Control, John Wiley and Sons, Ltd., New York, USA, 567~pp., 1996. Witten, I H. and Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 525~pp., 2000.