Abstract
Data-driven modelling approaches, like artificial neural networks, are particularly sensitive to the choice of input and output variables. This study focuses on the size of the temporal observation interval of input and output data in a river flow prediction application, analysing the simulation performances when considering increasing time aggregations of different input variables. The analyses are carried out on the data registered in a medium-sized (1,050 km2) watershed located on the Apennine mountains, where hourly meteorological data and streamflow measurements are available over an 8-year period.
Four modelling approaches are considered for the prediction of river flow in the closure section: (1) without exogenous inputs, (2) with the additional input of past precipitation data, (3) with the additional input of past streamflow data measured in the upstream section, (4) with the additional input of both past precipitation and past upstream flow. For each modelling approach, using both (a) input data and output data at the same time scale and (b) input data at a temporal resolution finer than that of the output data, optimal modelling networks are identified and forecast performances are compared. The results highlight how the simulation improves with the addition of exogenous inputs, in particular upstream flow data, and with the use of input data at a temporal resolution finer than that of the output data. The results also show how both such benefits increase for larger temporal aggregation of the forecasts.
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References
Abrahart RJ, See L (2000) Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrological Processes 14(11):2157–2172
Abrahart RJ, See L, Kneale PE (2001) Investigating the role of saliency analysis with a neural network rainfall-runoff model. Computers & Geosciences 27:921–928
Atiya AF, El-Shoura SM, Shaheen SI, El-Sherif MS (1999) A comparison between neural-network forecasting techniques – Case study: River flow forecasting. IEEE Transactions on neural networks 10(2):402–409
Cameron D, Kneale P, See L (2002) An evaluation of a traditional and a neural net modelling approach for flood forecasting for an upland catchment. Hydrological Processes 16(5):1033–1046
Campolo M, Andreussi P, Soldati A (1999) River flood forecasting with a neural network model. Water Resources Research 35(4):1191–1197
Chang F, Chen Y (2003) Estuary water-stage forecasting by using radial basis function neural network. Journal of Hydrology 270:158–166
Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Progress in Physical Geography 25(1):80–108
Deka P, Chandramouli V (2005) Fuzzy neural network model for hydrologic flow routing. Journal of Hydrologic Engineering 10(4):302–314
Hagan MT, Menhaj M (1994) Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5(6):989–993
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2:359–366
Hsu K, Gupta HV, Gao X, Sorooshian S, Imam B (2002) Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis. Water Resources Research 38(12) doi:10.1029/2001WR000795
Imrie CE, Durucan S, Korre A (2000) River flow prediction using artificial neural networks: generalisation beyond the calibration range. Journal of Hydrology 233:138–153
Jain A, Srinivasulu S (2004) Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques. Wat Resources Research 40 doi:10.1029/2003WR002355.
Karunanithi N, Grenney WJ, Whitley D, Bovee K (1994) Neural networks for river flow prediction. Journal of Computing in Civil Engineering 8(2):201–220
Laio F, Porporato A, Revelli R, Ridolfi L (2003) A comparison of nonlinear flood forecasting methods. Wat Resources Research 39(5) doi:10.1029/2002WR001551
Minns AW, Hall MJ (1996) Artificial neural networks as rainfall-runoff models. Hydrological Science Journal 41(3):399–417
Moradkhani H, Hsu K, Gupta HV, Sorooshian S (2004) Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. Journal of Hydrology 295:246–262
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part 1. A discussion of principles. Journal of Hydrology 10:282–290
Nagesh Kumar D, Srinivasa Raju K, Tathish T (2004) River flow forecasting using recurrent neural networks. Water Resources Management 18:143–161
Solomatine DP, Dulal KN (2003) Model trees as an alternative to neural networks in rainfall–runoff modelling. Hydrological Sciences Journal 48(3): 399–411
Toth E, Brath A (2007) Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling. Water Resources Research 43, W11405, doi:10.1029/2006WR005383.
Zealand CM, Burn DH, Simonovic SP (1999) Short term streamflow forecasting using artificial neural networks. Journal of Hydrology 214:32–48
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© 2009 Springer-Verlag Berlin Heidelberg
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Toth, E. (2009). Data-Driven Streamflow Simulation: The Influence of Exogenous Variables and Temporal Resolution. In: Abrahart, R.J., See, L.M., Solomatine, D.P. (eds) Practical Hydroinformatics. Water Science and Technology Library, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79881-1_9
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DOI: https://doi.org/10.1007/978-3-540-79881-1_9
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