Uncertainty Analysis of Artificial Neural Network and Fuzzy Neural Models in Rainfall-Runoff Simulation of Bashar River Basin

Document Type : Research Article

Authors

1 Assistant Professor, Department of Civil Engineering, Water Resources Management, Yasouj University

2 Graduated M.Sc. Student, Department of Civil Engineering, Faculty of Civil Engineering, Yasouj University, Iran

3 Ph.D. in Water Science and Engineering, Regional Water Company of Kohkiloyeh and Boyer Ahmad Province

4 PhD student, Department of Water and Wastewater, Shahid Beheshti University, Tehran, Iran

10.22059/ije.2024.367471.1769

Abstract

In this research, in order to select an appropriate model for predicting river flow in the Bashar River basin, data-driven models including multilayer perceptron artificial neural network and fuzzy neural network from the Sugeno fuzzy inference system were used using the clustering reduction method, and the analysis of uncertainty of these models was investigated.The data used in this research includes monthly values of rainfall and average temperature at rain gauge stations, as well as monthly average river discharge at the hydrological station located in the Bashar River basin from the years 1979-1980 to 2018-2019. The sensitivity analysis results on the number of neurons in the hidden layer of the neural network showed that the optimal number of neurons in the hidden layer for the input combination is 13.Based on the root mean square error (RMSE) index, the best combination of input variables for simulating river flow in both the neural network and neural-fuzzy network models was determined to be the input combination consisting of average river discharge with one-month and two-month lag along with monthly rainfall values and monthly rainfall values with one-month and two-month lag.In order to investigate the uncertainty of the models, the artificial neural network and neural-fuzzy network models were employed in the form of Monte Carlo sampling.The results of the uncertainty analysis showed that, for the same random input variables, the deviation from the mean in the output of the neural network model is higher than that of the neural-fuzzy network model. Additionally, the results obtained from calculating the confidence interval indicate that the confidence interval for different confidence levels is smaller in the neural-fuzzy network compared to the neural network. For example, in the neural network model with 98% confidence, the output is within the range of (0.64 and 0.36), whereas in the neural-fuzzy network model with 98% confidence, the output is between the range of (0.69 and 0.53). This indicates a higher level of uncertainty in the results of the neural network model.

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[1].          Abbaszadeh H, Norouzi R, Süme V, Daneshfaraz R, Tarinejad R.Discharge coefficient of combined rectangular-triangular weirs using soft computing models. Journal of Hydraulic Structures, 2023, 98–110.
[2].          Hassanzadeh Y, Abbaszadeh H. Investigating Discharge Coefficient of Slide Gate-Sill Combination Using Expert Soft Computing Models, Journal of Hydraulic Structures,2023, 63–80.
[3].          Aqil M, Kita I, Yano A, Nishiyama, S. Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool, Journal of environmental management, 2007, 215–223.
[4].          Eskandari A, Noori R, Vesali Naseh MR. Saeedi F. Uncertainty Evaluation of ANN and ANFIS Models in Inflow Forecasting into the Raees-Ali Delvari Dam,” Journal of Environmental Science and Technology, 2019, 31–47.
[5].          Nadiri AA, Yousefzadeh S. A Comparison of the Performance of Artificial Neural Network, Fuzzy Logic and Adaptive Neuro-Fuzzy Inference Systems Models in the Estimation of Aquifer Hydraulic Conductivity. A Case Study: Maraghe-Bonab Aquifer, Hydrogeomorphology, 2017, 21–40.
[6].          Razzaghzadeh Z, Nourani V, Behfar N. The conjunction of feature extraction method with AI-based ensemble statistical downscaling models, Amirkabir Journal of Civil Engineering, 2020.
[7].          Hassanzadeh Y, Moazamnia M, Sadeghfam S, Nadiri, A A. Hydraulic conductivity and uncertainty analysis of between-models and input data by using Bayesian model averaging of artificial intelligence model, Amirkabir Journal of Civil Engineering, 2020, 2171–2190.
[8].          Zeinalie M, golabi, M R, Niksokhan M H, Sharifi, M R. Modeling Daily River Flow Using Simulator Meta-Models (Case study: Gamasiab River), Journal of Environmental Science and Technology, 2020, 121–133.
[9].          Seifi, A, Ehteram, M, Singh, V P, Mosavi A. Modeling and uncertainty analysis of groundwater level using six evolutionary optimization algorithms hybridized with ANFIS, SVM, and ANN, Sustainability, 2020, 4023.
[10].        Riahi-Madvar H, Seifi A. Uncertainty analysis in bed load transport prediction of gravel bed rivers by ANN and ANFIS, Arabian Journal of Geosciences, 2018, 1–20.
[11].        Daneshfaraz R, Norouzi R, Ebadzadeh P, Di Francesco S, Abraham J P. Experimental study of geometric shape and size of sill effects on the hydraulic performance of sluice gates, Water, 2023, 314.
[12].        Abbaszade H, Norouzi R, Sume V, Kuriqi, Daneshfaraz R, Abraham J. Sill role effect on the flow characteristics (experimental and regression model analytical),” Fluids, 2023, 235.