Comparative evaluation of IHACRES, ANN and Linear Regression to simulate runoff in Tashk- Bakhtegan basin

Subject Areas : Subjects

Alireza Pilpayeh ¹ (Assistant Professor, Department of Civil Engineering, Parsabad Moghan Branch, Islamic Azad University, Parsabad, Iran.)
Aydin Bakhtar ² (Graduate M.Sc. student in Water Resources Management, Department of Water Engineering, Urmia University, Urmia, Iran.)
Akbar Rahmati ³ (Graduate M.Sc. student in the field of water resources management, Faculty of Agriculture, University of Tehran, Tehran, Iran.)
Afshin Shayeghi ⁴ (Graduate M.Sc. student in the field of water resources management, engineering faculty, water engineering department, Imam Khomeini International University, Qazvin, Iran.)

Received: 2019-08-13 Accepted : 2021-09-01 Published : 2022-07-23

Keywords: Neural network, flood, Artificial Intelligence, Rainfall- Runoff models,

Abstract :

Streamflow simulation is one of the most important issues in the field of hydrology and water resources. Forecasting of runoff in future periods can determine the rate of flooding and also the increasing and decreasing trend of river discharge. There are various methods to simulate or predict runoff rates, including statistical methods, rainfall-runoff models and artificial intelligence. In this study, three models consisting of neural network, IHACRES and linear regression were used to estimate runoff in Tashk-Bakhtegan basin and evaluated using statistics such as correlation coefficient, coefficient of determination and other error indices. The results showed that runoff and precipitation have a non-linear relationship and therefore, the linear regression model cannot be trusted to estimate runoff. The results also showed that, in general the neural network model due to the nonlinear relationship between precipitation and runoff compared to the other two models have good performance in estimating monthly runoff in Tashk-Bakhtegan basin.

References:

1.       Mengistu, K.T. Watershed Hydrological Responses to Changes in Land Use and Land Cover, and Management Practices at Hare Watershed, Ethiopia. 2009.

2.       Te Linde, A.H.; Aerts, J.; Hurkmans, R.; Eberle, M. Comparing Model Performance of Two Rainfall-Runoff Models in the Rhine Basin Using Different Atmospheric Forcing Data Sets. Hydrol. Earth Syst. Sci. 2008, 12, 943–957.

3.       Dye, P.J.; Croke, B.F.W. Evaluation of Streamflow Predictions by the IHACRES Rainfall-Runoff Model in Two South African Catchments. Environ. Model. Softw. 2003, 18, 705–712.

4.       Dakhlaoui, H.; Ruelland, D.; Tramblay, Y.; Bargaoui, Z. Evaluating the Robustness of Conceptual Rainfall-Runoff Models under Climate Variability in Northern Tunisia. J. Hydrol. 2017, 550, 201–217.

5.       Khazaee Poul, A.; Shourian, M.; Ebrahimi, H. A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction. Water Resour. Manag. 2019, 33, 2907–2923.

6.       Lotfirad, M.; Salehpoor Laghani, J.; Ashrafzadeh, A. Using the IHACRES Model to Investigate the Impacts of Changing Climate on Streamflow in a Semi-Arid Basin in North-Central Iran. J. Hydraul. Struct. 2019, 5, 27–41.

7.       Ahmadi, M.; Moeini, A.; Ahmadi, H.; Motamedvaziri, B.; Zehtabiyan, G.R. Comparison of the Performance of SWAT, IHACRES and Artificial Neural Networks Models in Rainfall-Runoff Simulation (case Study: Kan Watershed, Iran). Phys. Chem. Earth, Parts A/B/C 2019, 111, 65–77.

8.       Abolverdi, J.; Khalili, D. Probabilistic Analysis of Extreme Regional Meteorological Droughts by L-Moments in a Semi-Arid Environment. Theor. Appl. Climatol. 2010, 102, 351–366.

9.       Jakeman, A.J.; Hornberger, G.M. How Much Complexity Is Warranted in a Rainfall‐runoff Model? Water Resour. Res. 1993, 29, 2637–2649.

10.   Helsel, D.R.; Hirsch, R.M. Statistical Methods in Water Resources; Elsevier, 1992; Vol. 49; ISBN 0444885285.

11.   Bal, P.K.; Ramachandran, A.; Geetha, R.; Bhaskaran, B.; Thirumurugan, P.; Indumathi, J.; Jayanthi, N. Climate Change Projections for Tamil Nadu, India: Deriving High-Resolution Climate Data by a Downscaling Approach Using PRECIS. Theor. Appl. Climatol. 2016, 123, 523–535.

12.    فعالیان; روشن, ح.ن.; روشن, ح.ن.; شاهدی; شاهدی; قنبرپور; قنبرپور کالیبراسیون و ارزیابی مدل هیدرولوژیکی IHACRES  به منظور شبیه سازی جریان روزانه. آب و خاک 2011, 25.

13.    مقدم, ر.; حجازی; میراسدالله; بهبودی; عبداله واسنجی و ارزیابی عملکرد مدل IHACRES در شبیه سازی رواناب در زیرحوضه ی آبریز لنبران، اهرچای. هیدروژئومورفولوژی 2019, 6, 187–204.