Abstract
Long-term monthly flow forecasts are essential for decision making in a river basin system. Many studies have already been reported on monthly as well as seasonal forecast using artificial neural networks (ANNs). This study demonstrates that monthly forecasts can be significantly improved if the input variables in ANN models are chosen with due consideration, even if number of training patterns are less. Monthly forecast models up to 12-month lead-time have been developed for Mississippi River in USA. It is seen that direct forecast with only antecedent flows as inputs does not improve the result. It is better to develop individual models for each month separately with information from previous years for the same month. Further, the forecast is found to significantly improve if the difference in predicted and actual flows is also included as one of the input variables (i.e. error updating), particularly where there is a clearly observed pattern in the historical information.
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References
Shamseldin AY (2004) Hybrid neural network modeling solutions. In: Abrahart RJ, Kneale PE, See LM (eds) Neural networks for hydrological modeling. A.A Balkema Publishers, Leiden, pp 61–79
Atiya AF, El-Shoura SM, Shaheen SI, El-Sherif MS (1999) A comparison between neural-network forecasting techniques—case study: river flow forecasting. IEEE Trans Neural Netw 10(2):402–409
Sajikumar N, Thandaveswara BS (1999) A nonlinear rainfall-runoff model using an artificial neural network. J Hydrol 216:32–55
ASCE Task Committee (2000) Artificial neural networks in hydrology-1: preliminary concepts. J Hydrol Eng ASCE 5(2):115–123
ASCE Task Committee (2000) Artificial neural networks in hydrology-2: hydrologic applications. J Hydrol Eng ASCE 5(2):124–137
Sivapragasam C, Liong SY, Pasha MFK (2001) Rainfall and runoff forecasting with SSA-SVM approach. J Hydroinform 3(3):141–152
Dolling OR, Varas EA (2002) Artificial neural networks for stream flow prediction. J Hydraul Res 40(5):547–554
Barratti R, Cannas B, Fanni A, Pintus M, Sechi GM, Toreno N (2003) River flow forecast for reservoir management through neural networks. Neurocomputing 55(3):421–437
Huang W, Xu B, Amy CH (2004) Forecasting flows in Apalachicola River using neural networks. Hydrol Process 18:2545–2564
Kisi O (2004) River flow modeling using artificial neural networks. J Hydrol Eng 9(1):60–63
Sudheer KP, Jain A (2004) Explaining the internal behavior of artificial neural network river flow model. Hydrol Process 118(4):833–844
Cannas B, Fanni A, Tronci S (2005) River flow forecasting using neural networks and wavelet analysis. Geophys Res Abstr 7:08651
Akhtar MK, Corzo GA, van Andel SJ, Jonoski A (2009) 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. Hydrol Earth Syst Sci 13:1607–1618
Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374:294–306
Wu CL, Chau KW, Li YS (2009) Predicting monthly stream flow using data driven models coupled with data pre-processing techniques. Water Resour Res 45:W08432. doi:10.1029/2007WR006737
Bisht DCS, Raju MM, Joshi MC (2010) ANN based river stage-discharge modeling for Godavari River India. Comput Model New Technol 14(3):48–62
Maier HR, Jain A, Dandy GC, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Model Softw 25(8):891–909
Shamseldin AY (2010) Artificial neural network model for river flow forecasting in a developing country. J Hydroinform 12(1):22–35
Othman F, Naseri M (2011) Reservoir inflow forecasting using artificial neural network. Int J Phys Sci 6(3):434–440
Yilmaz AG, Imteaz MA, Jenkins G (2011) Catchment flow estimation using artificial neural networks in the mountainous Euphrates Basin. J Hydrol 410:134–140
Bowden GJ, Dandy GC, Maier HR (2005) Input determination for neural network models in water resources applications. Part 1—background and methodology. J Hydrol 301:75–92
Wang WC, Chau KW, Cheng CT, Qui L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374:294–306
Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly stream flow forecasting. J Hydrol 399:132–140
Wu CL, Chau KW, Li YS (2009) Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques. Water Resour Res 45(8):159–175
Iritz L (1992) Rainfall input in an adaptive river flow forecast model. Hydrol Sci J 37(6):607–620
Xiong l, O’Connor KM (2002) Comparison of four updating models for real-time river flow forecasting. Hydrol Sci 47(4): 621–639. http://www.mvs.usace.army.mil/Our%20Mississippi/ourmississippi_su11_lowres.pdf
Haykin S (1999) Neural network: a comprehensive foundation, 2nd edn. Prentice Hall, New Jersey
USACE (2011) Great flood of’11. Our Mississippi. Rock Island, IL: US Army Corps of Engineers (viewed March 2012)
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Sivapragasam, C., Vanitha, S., Muttil, N. et al. Monthly flow forecast for Mississippi River basin using artificial neural networks. Neural Comput & Applic 24, 1785–1793 (2014). https://doi.org/10.1007/s00521-013-1419-6
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DOI: https://doi.org/10.1007/s00521-013-1419-6