A Comparison of ANN and HSPF Models for Runoff Simulation in Balkhichai River Watershed, Iran


In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto- and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF.

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Amirhossien, F. , Alireza, F. , Kazem, J. and Mohammadbagher, S. (2015) A Comparison of ANN and HSPF Models for Runoff Simulation in Balkhichai River Watershed, Iran. American Journal of Climate Change, 4, 203-216. doi: 10.4236/ajcc.2015.43016.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Kahya, E. and Dracup, J.A. (1993) US Stream Flow Patterns in Relation to the El Nino/Southern Oscillation. Water Resources Research, 28, 491-503.
[2] Dawson, C.W. and Wilby, R.L. (2001) Hydrological Modeling Using Artificial Neural Networks. Progress in Physical Geography, 25, 80-108.
[3] Han, D., Chan, L. and Zhu, N. (2007) Flood Forecasting Using Support Vector Machine. Journal of Hydroinformatics, 9, 267-276.
[4] Han, D., Kwong, T. and Li, S. (2007) Uncertainties in Real-Time Flood Forecasting with Neural Networks. Hydrological Processes, 21, 223-228.
[5] Goel, A. (2009) ANN Based Modeling for Prediction of Evaporation in Reservoirs (Research Note). International Journal of Engineering, Transcations A: Basics, 22, 351-358.
[6] Banaei, F.K., Zinatizadeh, A.A.L., Mesgar, M. and Salari, Z. (2012) Dynamic Performance Analysis and Simulation of a Full Scale Activated Sludge System Treating an Industrial Wastewater Using Artificial Neutral Network. International Journal of Engineering, Transcations A: Basics, 26, 465-472.
[7] Nayak, P.C., Sudheer, K.P., Rangan, D.M. and Ramasastri, K.S. (2004) A Neuro-Fuzzy Computing Technique for Modelling Hydrological Time Series. Journal of Hydrology, 291, 52-66.
[8] Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353.
[9] Bahramifar, A., Shirkhani, R. and Mohammadi, M. (2013) An Anfis-Based Approach for Prediction the Manning Roughness Coeffinet in Alluvial Channel at the Bank-Full Stage. International Journal of Engineering, Transcations B: Basics, 26, 177-186.
[10] Shamseldin, A.Y. (1997) Application of a Neural Network Technique to Rainfall-Runoff Modeling. Journal of Hydrology, 199, 272-294.
[11] Kumar, A.R.S., Sudheer, K.P., Jain, S.K. and Agarwal, P.K. (2005) Rainfall-Runoff Modeling Using Artificial Neural Networks: Comparison of Network Types. Hydrological Processes, 19, 1277-1291.
[12] Mutlu, E., Chaubey, I., Hexmoor, H. and Bajwa, S.G. (2008) Comparison of Artificial Neural Network Models for Hydrologic Predictions at Multiple Gauging Stations in an Agricultural Watershed. Hydrological Processes, 22, 5097-5106.
[13] Halff, A.H., Halff, H.M. and Azmoodeh, M. (1993) Predicting Runoff from Rainfall Using Neural Networks. In: Proceedings of the Engineering Hydrology, American Society of Civil Engineers, New York, 760-765.
[14] Filho, A.J.P. and dos Santos, C.C. (2006) Modeling a Densely Urbanized Watershed with an Artificial Neural Network, Weather Radar and Telemetric Data. Journal of Hydrology, 317, 31-48.
[15] Zhang, B. and Govindaraju, R.S. (2000) Prediction of Watershed Runoff Using Bayesian Concepts and Modular Neural Networks. Water Resources Research, 36, 753-762.
[16] Kingston, G.B., Maier, H.R. and Lambert, M.F. (2005) Calibration and Validation of Neural Networks to Ensure Physically Plausible Hydrological Modeling. Journal of Hydrology, 314, 159-176.
[17] Abrahart, R.J. and See, L.M. (2007) Neural Network Emulation of a Rainfall-Runoff Model. Hydrology and Earth System Sciences, 4, 288-326.
[18] Srinivasan, M.S., Hamlett, J.M., Day, R.L., Sams, J.I. and Peterson, G.W. (1998) Hydrologic Modeling of Two Glaciated Watersheds in Northeast Pennsylvania. Journal of the American Water Resources Association, 34, 963-978.
[19] Zarriello, P.J. and Ries III, K.G. (2000) A Precipitation-Runoff Model for Analysis of the Effects of Water Withdrawals on Streamflow, Ipswich River Basin, Massachusetts. USGS Water-Resources Investigations Report 00-4029, 99 p.
[20] Fontaine, T.A. and Jacomino, V.M.F. (1997) Sensitivity Analysis of Simulated Contaminated Sediment Transport. Journal of the American Water Resources Association, 33, 313-326.
[21] Donigian Jr., A.S., Chinnaswamy, R.V. and Jobes, T.H. (1997) Conceptual Design of Multipurpose Detention Facilities for Flood Protection and Nonpoint Source Pollution Control. Aqua Terra Consultants, Mountain View, 151 p.
[22] Abdulla, F., Eshtawi, T. and Assaf, H. (2009) Assessment of the Impact of Potential Climate Change on the Water Balance of a Semi-Arid Watershed. Water Resources Management, 23, 2051-2068.
[23] Al-Abed, N.A. and Whiteley, H.R. (2002) Calibration of the Hydrological Simulation Program Fortran (HSPF) Model Using Automatic Calibration and Geographical Information Systems. Hydrological Processes, 16, 3169-3188.
[24] Tokar, A.S. and Markus, M. (2000) Precipitation-Runoff Modelling Using Artificial Neural Networks and Conceptual Models. Journal of Hydrologic Engineering, 4, 232-239.
[25] Morid, S., Gosain, A.K. and Keshari, A.K. (2002) Comparison of the SWAT Model and ANN for Daily Simulation of Runoff in Snowbound Ungauged Catchments. Proceedings of the Fifth International Conference on Hydro Informatics, Cardiff, 1-5 July 2002.
[26] Srivastava, P., McNair, J.N. and Johnson, T.E. (2006) Comparison of Process-Based and Artificial Neural Network Approaches for Streamflow Modelling in an Agricultural Watershed. Journal of the American Water Resources Association, 42, 545-563.
[27] Lee, H., Lin, Y. and Chiu, Y. (2006) Quantitative Estimation of Reservoir Sedimentation from Three Typhoon Events. Journal of Hydrologic Engineering, 11, 362-370.
[28] Wang, P.F., Skahill, B., Samaitis, H. and Johnston, R. (2002) GIS-Based Artificial Neural Network and Processed-Based HSPF Model for Watershed Runoff in Sinclair and Dyes Inlet, WA. Proceedings of the Water Environment Federation, Watershed, 1547-1564.
[29] Crawford, H.H. and Linsley, R.K. (1966) Digital Simulation in Hydrology: Stanford Watershed Model IV. Technical Report No. 39, Department of Civil and Environmental Engineering, Stanford University, Stanford.
[30] Donigian, A.S. and Davis, H.H. (1978) User’s Manual for Agricultural Runoff Management (ARM) Model. EPA-600/3-78-080, USEPA, Athens, GA, 112 p.
[31] Donigian, A.S. and Crawford, N.H. (1976) Modelling Nonpoint Pollution from the Land Surface. EPA/600/3-76-083, Environmental Research Laboratory, Athens.
[32] Hydrocomp, Inc. (1977) Hydrocomp Water Quality Operations Manual. Hydrocomp, Inc, Palo Alto.
[33] Donigian Jr., A.S. and Huber, W.C. (1991) Modeling of Nonpoint Source Water Quality in Urban and Non-Urban Areas. EPA-600/3-91-039, USEPA, Athens, GA, 78 p.
[34] Donigian Jr., A.S., Bicknell, B.R. and Imhoff, J.C. (1995) Hydrological Simulation Program—FORTRAN (HSPF). In: Singh, V.P., Ed., Computer Models of Watershed Hydrology, Water Resources Pubs, Highlands Ranch, 395-442.
[35] Bicknell, B.R., Imhoff, J.C., Kittle, J.L., Jobes, T.H. and Donigian, A.S. (2005) Hydrological Simulation Program FORTRAN. Version 12.2, User’s Manual.
[36] Albek, M., Ogutveren, U. and Albek, E. (2004) Hydrological Modeling of Seydi Suyu Watershed (Turkey) with HSPF. Journal of Hydrology, 285, 260-271.
[37] Mark, S.J., William, F.C., Vishal, K.M., Tammo, S.S., Erin, S.B. and Jan, B. (2003) Application of Two Hydrologic Models with Different Runoff Mechanisms to a Hillslope Dominated Watershed in the Northeastern US: A Comparison of HSPF and SMR. Journal of Hydrology, 284, 57-76.
[38] Bicknel, B.R., Imhoff, J.C., Kittle, J.L., Donigian, A.S. and Johanson, R.C. (1993) Hydrological Simulation Program-Fortran User’s Manual for Release 10. EPA/600/R-93/174, Environmental Research Laboratory Office of Research and Development, US Environmental Protection Agency, Athens.
[39] Donigian, A.S., Imhoff, J.C., Bicknell, B.R. and Kittle, J.L. (1984) Application Guide for Hydrological Simulation Program-Fortran (HSPF). EPA-600/3-84-065, United States Environmental Protection Agency, Athens, GA.
[40] EPA (2001) BASINS Technical Note 6, Estimating Hydrology and Hydraulic Parameters for HSPF, US.
[41] Eluyode, O.S. and Akomolafe, D.T. (2013) Comparative Study of Biological and Artificial Neural Networks. European Journal of Applied Engineering and Scientific Research, 2, 36-46.
[42] De Vos, N.J. and Rientjes, T.H.M. (2005) Constraints of Artificial Neural Networks for Rainfall-Runoff Modeling: Trade-Offs in Hydrological State Representation and Model Evaluation. Hydrology and Earth System Sciences, 9, 111-126.
[43] Nash, J.E. and Sutcliffe J.V. (1970) River Flow Forecasting through Conceptual Models Part I—A Discussion of Principles. Journal of Hydrology, 10, 282-290.
[44] Linsley, R.K., Kohler, M.A. and Paulhus, J.L.H. (1988) Hydrology for Engineers. McGraw-Hill, New York.
[45] Bougadis, J., Adamowski, K. and Diduch, R. (2005) Short-Term Municipal Water Demand Forecasting. Hydrological Processes, 19, 137-148.
[46] Cigizoglu, H.K. (2005) Generalized Regression Neural Network in Monthly Flow Forecasting. Civil Engineering and Environmental Systems, 22, 71-81.
[47] Moradkhani, H., Hsu, K.-L., Gupta, H.V. and Sorooshian, S. (2004) Improved Streamflow Forecasting Using Self-Organizing Radial Basis Function Artificial Neural Networks. Journal of Hydrology, 295, 246-262.

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