Evaluation of two combined hydrological-black box models for flood forecasting in Halilrud river basin

Document Type : Research Article

Authors

1 Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

2 Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman,, Iran

Abstract

Forecasting river flow in flood conditions is an essential part of river engineering studies that, if accurately estimated, can greatly contribute to the effectiveness of management programs and reduce financial and human losses. Using appropriate models and increasing the accuracy of these models will lead to improvements in the accuracy of prediction results. One of the available solutions to increase the accuracy of existing and valid rainfall-runoff models is to build a combined model with the help of preferably intelligent methods link to these models. In the present study, the combination of WEAP 21 hydrological model with black box models based on ANN and GMDH methods is used to increase the accuracy of WEAP model and then the created model was used to simulate the flood of part of Halilrud river in Kerman province. Precipitation, flow, humidity, wind and temperature data were entered into the WEAP model for the existing record minus the last two years. After calibrating and validating the model, the last two years were forecasted. The results showed that the WEAP-ANN (R2 = 0.78) model was able to estimate the runoff with higher accuracy compared to the WEAP-GMDH (R2 = 0.59) and WEAP (R2 = 0.14) models in the basin.

Keywords


[1]. Zabbah I, Roshani AR, Khafage A. Prediction of monthly rainfall using artificial neural network mixture approach, Case Study: Torbat-e Heydariyeh. Journal of the Earth and Space Physics. 2019;44(4):115-126.
[2]. Dottori F, Salamon P, Bianchi A, Alfieri L, Hirpa FA, Feyen L. Development and evaluation of a framework for global flood hazard mapping. Advances in Water Resources. 2016;94: 87-102.
[3]. Bruen M, Yang J. Combined hydraulic and black-box models for flood forecasting in urban drainage systems. Journal of Hydrologic Engineering. 2006;11(6):589-596.
[4]. Bisht DCS, Raju MM, Joshi MC. ANN based river stage-discharge modeling for Godavari River, India. Comput Model New Technol. 2010;14(3):48-62.
[5]. Shadmani M, Marofi S, Mohammadi K, Sabziparvar AA. Regional flood discharge modeling in Hamedan province using Artificial Neural Network. Journal of Water and Soil Conservation. 2011;18(4):21-42.
[6]. Banihabib M E. Performance of conceptual and black-box models in flood warning systems. Cogent engineering. 2016;3(1):1-13.
[7]. Kalteh AA. Enhanced Monthly Precipitation Forecasting Using Artificial Neural Network and Singular Spectrum Analysis Conjunction Models. Indian National Academy of Engineering. 2017;2 :73-81.
[8]. Asadi H, Shahedi K, Jarihani B, Sidle R.C. Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach. Water. 2019;11, 212:1-20.
[9]. Gouda KC, R L, Kumari P, Sharma M, Nair AD. An Approach for Rainfall Prediction using Soft Computing. International Journal of Engineering Trends and Technology. 2019;67(3):158-164.
[10]. Dodangeh E, Panahi M, Rezaie F, Lee S, Tien Bui D, Lee CW, Pradhan B. Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search. Journal of Hydrology. 2020;1-14.
[11]. Aghelpour P, Varshavian V. Evaluation of
stochastic and artificial intelligence models in modeling and predicting of river daily flow time series. Stochastic Environmental Research and Risk Assessment. 2020;34(1):33-50.
[12]. Stockholm Environment Institute (SEI). (2016); Water evaluation and planning system, WEAP. Stockholm Environment Institute, Boston, USA, from http://www.weap21.org
[13]. Ahmadaali J, Barani GA, Qaderi K, Hessari B. Analysis of the Effects of Water Management Strategies and Climate Change on the Environmental and Agricultural Sustainability of Urmia Lake Basin, Iran. Water. 2018;10(160):1-21.
[14]. Poonia V, Lal Tiwari H. Rainfall-runoff modeling for the Hoshangabad Basin of Narmada River using artificial neural network. Arabian Journal of Geosciences. 2020;13(944):1-10.
[15]. Ashrafzadeh A, Ki┼či O, Aghelpour P, Biazar SM, Askarizad Masouleh M. Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran. J. Irrig. Drain Eng. 2020;146(6):1-10.
[16]. Tian J, Liu J, Wang Y, Wang W, Li C, Hu C. A coupled atmospheric–hydrologic modeling system with variable grid sizes for rainfall-runoff simulation in semi-humid and semi-arid watersheds: how does the coupling scale affects the results. Hydrol. Earth Syst. Sci. 2020;24: 3933-3949.
[17]. Bahrami H, Emamgholi Zadeh S. Prediction of suspended sediment distribution of Karoon River using artificial neural network. Journal of Marine Science and Technology. 2018;17(2):27-35. [Persian]
[18]. Zhongda T, Shujiang L, Yanhong W, Yi S. A prediction method based on wavelet transform and multiple models fusion for chaotic time series. Chaos. Solitons and Fractals. 2017;98: 158-172.
[19]. Molaie Zadeh SF, Moradi M H. Nervous chaotic fuzzy sets and systems. Computational intelligence in electrical engineering. 2014;5(1):41-56. [Persian]
[20]. Zhu L, Wang Y, Fan Q. MODWT-ARMA model for time series prediction. Applied Mathematical Modelling. 2014;38: 1859-1865.
Volume 8, Issue 2
July 2021
Pages 397-409
  • Receive Date: 30 November 2020
  • Revise Date: 12 May 2021
  • Accept Date: 12 May 2021
  • First Publish Date: 16 June 2021