Abstract
Hydrological models are widely used for the simulation of stream flow in order to aid water resources planning and management in catchment or river basin. Numerous hydrological models have been developed based on different theories. Performance of such models depends on hydro-climatic setting of a catchment. In the present study, performance of a widely used physically based distributed model known as Soil and Water Assessment (SWAT) and a data-driven model, namely hybrid artificial neural network (HANN), has been evaluated to simulate stream flow in an arid catchment located in the south of Iran. Data related to topography, hydrometeorology, land cover, and soil were collected and processed for this purpose. The models were calibrated and validated with same time period to evaluate the advantage and disadvantages of different models. The results showed SWAT outperformed HANN in terms of relative errors such as Nash-Sutcliffe efficiency and percent of bias during model validation. Other error indicates, namely root mean square error (RMSE), mean square error, and mean relative error (MRE), were found close to zero for SWAT during both model calibration and validation. The study suggests that both models have their own promising flow prediction due to their own features and capabilities for daily flow.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Patra KC (2008) Hydrology and water resources engineering. Alpha Science International Ltd., U.K.
World Water Council, Water crisis. Retrieved 20 Dec 2009, from http://www.worldwatercouncil.org/index.php?id=25
Foltz RC (2002) Iran’s water crisis: cultural, political, and ethical dimensions. J Agric Environ Ethics 15:357–380
Shaw EM (1994) Hydrology in practice. Chapman and Hall, London
Singh A, Imtiyaz M, Isaac RK, Denis DM (2012) Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India. Agric Water Manage 104:113–120
Van Liew MW, Arnold JG, Garbrecht JD (2003) Hydrologic simulation on agricultural watersheds: choosing between two models. Trans ASAE 46(6):1539–1551
Anctil F, Perrin C, Andreassian V (2004) Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models. Environ Model Softw 19:357–368
Junfeng C, Xiubin L, Ming Z (2005) Simulating the impacts of climate variation and land-cover changes on basin hydrology: a case study of the Suomo basin. Scii China Ser D Earth Sci 48(9):1501–1509
Qin XU, Ren L, Yu Z, Bang Y, Wang G (2008) Rainfall-runoff modelling at daily scale with artificial neural networks. In: 4th international conference on natural computation, vol. 2, pp. 504-508, ICNC. 18–20 Oct
Parajuli PB, Nelson NO, Frees LD, Mankin KR (2009) Comparison of AnnAGNPS and SWAT model simulation results in USDA-CEAP agricultural watersheds in south-central Kansas. Hydrol Process 23:785–797
Xu ZX, Pang JP, Liu CM, Li JY (2009) Assessment of runoff and sediment yield in the Miyun reservoir catchment by using SWAT model. Hydrol Process 23:3619–3630
Demirel MC, Venancio A, Kahya E (2009) Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Adv Eng Softw 40:467–473
Srivastava P, McNair JN, Johnson TE (2006) Comparison of process-based and artificial neural network approaches for stream flow modeling in an agricultural watershed. J Am Water Resour Assoc 42(3):545–563
Morid S, Gosain AK, Keshari AK (2002) Comparison of the SWAT model and ANN for daily simulation of runoff in snowbound un-gauged catchments. In: Fifth international conference on hydroinformatics, Cardiff, UK
Al-Damkhi AM, Abdul-Wahab SA, AL-Nafisi AS (2009) On the need to reconsidering water management in Kuwait. Clean Technol Environ Policy 11:379–384
Kanae S (2009) Global warming and the water crisis. J Health Sci 55:860–864
Jajarmizadeh M, Harun S, Salarpour M (2014) An assessment of a proposed hybrid neural network for daily flow prediction in arid climate. Model Simul Eng Article ID 635018, 10 pages
Jajarmizadeh M, Harun S, Abdullah R, Salarpour M An evaluation of blue water prediction in southern part of Iran using SWAT. Environ Eng Manage J (in press)
Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Santhi C, Harmel RD, van Griensven A, Van Liew MW, Kannan N, Jha M (2012) SWAT: model use, calibration, and validation. Am Soc Agric Biol Eng 55(4):1491–1508
Jajarmizadeh M, Harun Sobri, Akib Shatirah, Sabari NSB (2014) Derivative discharge and runoff volume simulation from different time steps with a hydrologic simulator. Res J Appl Sci Eng Technol 8(9):1125–1131
Jajarmizadeh M, Harun S, Shahid S, Akib S, Salarpour M (2014) Impact of direct soil-moisture and revised soil-moisture index methods on hydrologic predictions in an arid climate. Adv Meteorol 2014:8, Article ID 156172
Jajarmizadeh M, Kakaei Lafdani E, Harun S, Ahmadi A (2015) Application of SVM and SWAT models for monthly stream flow prediction a case study in South of Iran. KSCE J Civil Eng 19(1):345–357
Ahmed Suliman AH, Jajarmizadeh M, Harun S, Darus IZM (2015) Comparison of semi-distributed, GIS-based hydrological models for the prediction of streamflow in a large catchment. Water Resour Manage 29(9):3095, 3110
Abbaspour KC, Johnson A, Van Genuchten MT (2004) Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J 3(4):1340–1352
Abbaspour KC (2015) SWAT‐CUP: SWAT calibration and uncertainty programs, A User Manual
Dawson CW, Wilby RL (2001) Hydrological modeling using artificial neural networks. Progress Phys Geogr 25(1):80–108
Bowden G, 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
Kalteh AM, Hjorth P, Berndtsson R (2008) Review of the self-organizing map (SOM) approach in water resources: analysis, modeling and application. Environ Model Softw 23:835–845
Parasuraman K, Elshorbagy A, Carey SK (2006) Spiking modular neural networks: a neural network modeling approach for hydrological processes. Water Resou Res 42:1–14
Krause P, Boyle DP, Base F (2005) Comparison of different efficient criteria for hydrological model assessment. Adv Geosci 5:89–97
Wu JS, Han J, Annambhotla S, Bryant S (2005) Artificial neural networks for forecasting watershed runoff and stream flows. J Hydrol Eng 10(3):216–222
Acknowledgments
This study is involved with the cooperation of Department of Hydraulic and Hydrology and Centre of Information and Communication Technology of Universiti Teknologi, Malaysia; consultant engineers of Ab Rah Saz Shargh Corporation in Iran; and the Regional Water, Agricultural, and Natural Resources Organizations of The Hormozgan State, Iran.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Jajarmizadeh, M., Sidek, L.M., Harun, S., Shahid, S., Basri, H. (2016). Comparison of a Hybrid Neural Network and Semi-distributed Simulator for Stream Flow Prediction. In: Tahir, W., Abu Bakar, P., Wahid, M., Mohd Nasir, S., Lee, W. (eds) ISFRAM 2015. Springer, Singapore. https://doi.org/10.1007/978-981-10-0500-8_10
Download citation
DOI: https://doi.org/10.1007/978-981-10-0500-8_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0499-5
Online ISBN: 978-981-10-0500-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)