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Flow forecast by SWAT model and ANN in Pracana basin, Portugal. (English) Zbl 1419.90059

Summary: This study provides a unique opportunity to analyze the issue of flow forecast based on the soil and water assessment tool (SWAT) and artificial neural network (ANN) models. In last two decades, the ANNs have been extensively applied to various water resources system problems. In this study, the ANNs were applied to the daily flow of the Pracana basin in Portugal. The comparison of ANN models and a process-based model SWAT was established based on their prediction accuracy. The ANN model was found to be more successful than the SWAT in relation to better forecast of peak flow. Nevertheless the SWAT model results revealed a better value of mean squared error. The results of this study, in general, showed that ANNs can be powerful tools in daily flow forecasts.

MSC:

90B90 Case-oriented studies in operations research
90B05 Inventory, storage, reservoirs
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[1] Altunkaynak, A.: Forecasting surface water level fluctuations of lake Van by artificial neural networks, Water resour manage 21, No. 2, 399-408 (2007)
[2] Altunkaynak, A.; Ozger, M.; Sen, Z.: Regional streamflow estimation by standard regional dependence function approach, J hydraul eng – ASCE 131, No. 11, 1001-1006 (2005)
[3] Anctil, F.; Rat, A.: Evaluation of neural network streamflow forecasting on 47 watersheds, J hydrol eng 10, No. 1, 85-88 (2005)
[4] Arnold J, Williams J, Srinivasan R, King K. SWAT – soil and water assessment tool – documentation and users manual. USDA-ARS, Temple, Texas; 1996.
[5] Arnold, J. G.; Fohrer, N.: SWAT2000: current capabilities and research opportunities in applied watershed modelling, Hydrol process 19, No. 3, 563-572 (2005)
[6] Asce: Task committee on application of anns in hydrology, artificial neural networks in hydrology. I: preliminary concepts, J hydrol eng 5, No. 2, 115-123 (2000)
[7] Asce: Task committee on application of anns in hydrology, artificial neural networks in hydrology. II: hydrology application, J hydrol eng 5, No. 2, 124-137 (2000)
[8] Baratti, R.; Cannas, B.; Fanni, A.; Pintus, M.; Sechi, G.; Toreno, N.: River flow forecast for reservoir management through neural networks, Neurocomputing 55, No. 3, 421-437 (2003)
[9] Burlando, P.; Rosso, R.; Cadavid, L. G.; Salas, J. D.: Forecasting of short-term rainfall using ARMA models, J hydrol 144, No. 1 – 4, 193-211 (1993)
[10] Calvoa, I.; Portelab, M.: Application of neural approaches to one-step daily flow forecasting in portuguese watersheds, J hydrol 332, No. 1 – 2, 1-15 (2007)
[11] Can, I.: A new improved na/k geothermometer by artificial neural networks, Geothermics 31, No. 6, 751-760 (2002)
[12] Chen, J.; Adams, B.: Integration of artificial neural networks with conceptual models in rainfall – runoff modeling, J hydrol 318, No. 1 – 4, 232-249 (2006)
[13] Demuth, H.; Beale, M.: Neural network toolbox for use with Matlab user’s guide, (2001)
[14] Di Luzio, M.; Arnold, J.; Srinivasan, R.: Effect of GIS data quality on small watershed stream flow and sediment simulations, Hydrol process 19, No. 3, 629-650 (2005)
[15] Govender, M.; Everson, C.: Modelling streamflow from two small south african experimental catchments using the SWAT model, Hydrol process 19, No. 3, 683-692 (2005)
[16] Govindaraju, R. S.; Rao, A. Ramachandra: Artificial neural networks in hydrology, (2000)
[17] Hsu, K. -L.; Gupta, H.; Sorooshian, S.: Artificial neural network modeling of the rainfall runoff process, Water resour res 31, No. 10, 2517-2530 (1995)
[18] Jha, M.; Pan, Z.; Takle, E.; Gu, R.: Impacts of climate change on streamflow in the upper mississippi river basin: a regional climate model perspective, J geophys res (2004)
[19] Kahya, E.; Dracup, J. A.: US streamflow patterns in relation to the el nino/southern oscillation, Water resour res 28, No. 8, 2491-2503 (1993)
[20] Karabork, C.; Kahya, E.: Multivariate stochastic modeling of streamflows in the sakarya basin, Turkish J eng environ sci 23, No. 2, 133-147 (1999)
[21] Kaur, R.; Srinivasan, R.; Mishra, K.; Dutta, D.; Prasad, D.; Bansal, G.: Assessment of SWAT model for soil and water management in India land use, Water resour res 3, 1-7 (2003)
[22] Kisi, O.: Multi-layer perceptrons with Levenberg – Marquardt training algorithm for suspended sediment concentration prediction and estimation, Hydrol sci J 49, No. 6, 1025-1040 (2004)
[23] Kisi, O.: Evapotranspiration estimation using feed-forward neural networks, Nordic hydrol 37, No. 3, 247-260 (2006)
[24] Kisi, O.: Streamflow forecasting using different artificial neural network algorithms, J hydrol eng 12, No. 5, 532-539 (2007)
[25] Kisi, O.: River flow forecasting and estimation using different artificial neural network techniques, Hydrol res 39, No. 1, 27-40 (2008)
[26] Lee, H.; Zehe, E.; Sivapalan, M.: Predictions of rainfall – runoff response and soil moisture dynamics in a micro-scale catchment using the crew model, Hydrol Earth syst sci 11, 819-849 (2007)
[27] Maier, H. R.; Dandy, G. C.: Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environ modell software 15, 101-124 (2000)
[28] Markus M. Application of neural networks in streamflow forecasting. Ph.D. thesis, Colorado State University; 1997.
[29] Moon, J.; Srinivasan, R.; Jacobs, J.: Stream flow estimation using spatially distributed rainfall in the trinity river basin, Texas, Trans ASAE 47, No. 5, 1445-1451 (2004)
[30] Morid S, Gosain AK, Keshari AK. Comparison of the SWAT model and ANN for daily simulation of runoff in snowbound ungauged catchments. In: Fifth international conference on hydroinformatics, Cardiff, UK; 2002.
[31] Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.: Learning representations by back-propagating errors, Nature 323, 533-536 (1986) · Zbl 1369.68284
[32] Salas, J. D.; Markus, M.; Tokar, A. S.: Streamflow forecasting based on artificial neural networks, Artificial neural networks in hydrology, 23-51 (2000)
[33] Silverman, D.; Dracup, J. A.: Artificial neural networks and long-lead precipitation prediction in California, J appl meteorol 31, No. 1, 57-66 (2000)
[34] Sivakumar, B.; Jayawardena, A. W.; Fernando, T. M. K.G.: River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches, J hydrol 265, No. 1 – 4, 225245 (2002)
[35] Srinivasan, R.; Arnold, J.: Integration of a basin-scale water quality model with GIS, Water resour bull 30, No. 3, 453-462 (1994)
[36] Srivastava, P.; Mcnair, J. N.; Johnson, T. E.: Comparison of process-based and artificial neural network approaches for streamflow modelling in an agricultural watershed, J am water resour assoc 42, No. 3, 545-563 (2006)
[37] Tokar, A. S.; Markus, M.: Precipitation – runoff modelling using artificial neural networks and conceptual models, J hydrol eng 4, No. 3, 232-239 (2000)
[38] Toth, E.; Brath, A.; Montanari, A.: Comparison of short-term rainfall prediction models for real-time flood forecasting, J hydrol 239, No. 1 – 4, 132-147 (2000)
[39] Venancio A, Martins F, Chambel P, Neves R. Modelacao hidrologica da bacia drenante da albufeira de pracana Faro: V Congresso Iberico; 4 – 8 December, 2006.
[40] Wu, J. S.; Han, J.; Annambhotla, S.; Bryant, S.: Artificial neural networks for forecasting watershed runoff and stream flows, J hydrol eng 10, No. 3, 216-222 (2005)
[41] Zealand, C. M.; Burn, D. H.; Simonovic, S. P.: Short term streamflow forecasting using artificial neural networks, J hydrol 214, No. 1 – 4, 3248 (1999)
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