Comparative Analysis of Multi-Linear Lag Cascade Model with Advanced Hydrological Techniques for Flood Prediction in Zarineh Rud River, Iran

Document Type : Research Article

Author

Department of Civil Engineering, University of Maragheh, Maragheh, Iran.

Abstract

The study focuses on the detailed methods of flood prediction in the Zarineh Rud River within Urmia Lake, Iran. It compared five different methods: the Multi-Linear Lag Cascade model, Saint-Venant equations, and three soft computing methods, namely Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Support Vector Machines. In this study, the data of the 2022 flood recorded at Alasaql and Safakhaneh stations were used. The performances of the models were then evaluated in terms of various statistical criteria such as the Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Peak Flow Ratio (PFR), and Percent Error in Peak (PEP). In general, it was found that the soft computing techniques, in particular ANN and ANFIS, are representing the best performance with NSE values of 0.938 and 0.935, respectively. Similarly, the MLLC model showed competitive performance with a value of NSE equal to 0.922 but with much lower computational time. The Saint-Venant model was somewhat less accurate, with an NSE value of 0.901 but with higher robustness against input uncertainty. For all models, results are better for the high flow range which is of importance for flood forecasting. Sensitivity analysis has shown that soft computing methods are more sensitive to input data errors than physically based Saint-Venant. This work underlines several critical trade-offs when optimizing model accuracy, computational efficiency, and robustness to uncertainty for flood prediction. The results highlight that soft computing methods, particularly ANN and ANFIS, are recommended for applications requiring high prediction accuracy and where high-quality input data is available. These insights can directly inform the development and implementation of flood warning systems in the Zarineh Rud River basin and similar hydrological systems worldwide.

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References

[1] J. Alhumoud, Analysis and evaluation of flood routing using Muskingum method, Journal of Applied Engineering Science, 20(4) (2022) 1366-1377.
[2] J. Chabokpour, Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques, Water Harvesting Research,  (2024).
[3] j. chabokpour, Y. Azhdan, Extraction of an analytical solution for flood routing in the river reaches (case study of Simineh River), Journal of Hydraulics, 15(2) (2020) 113-130.
[4] X.-m. Song, F.-z. Kong, Z.-x. Zhu, Application of Muskingum routing method with variable parameters in the ungauged basin, Water Science and Engineering, 4(1) (2011) 1-12.
[5] H.M. Samani, G. Shamsipour, Hydrologic flood routing in branched river systems via nonlinear optimization, Journal of Hydraulic Research, 42(1) (2004) 55-59.
[6] N. Cortés-Salazar, N. Vásquez, N. Mizukami, P.A. Mendoza, X. Vargas, To what extent does river routing matter in hydrological modeling?, Hydrology and Earth System Sciences, 27(19) (2023) 3505-3524.
[7] R. Moussa, C. Bocquillon, Criteria for the choice of flood-routing methods in natural channels, Journal of Hydrology, 186(1-4) (1996) 1-30.
[8] C. Yoo, J. Lee, M. Lee, Parameter estimation of the Muskingum channel flood-routing model in ungauged channel reaches Journal of Hydrologic Engineering, 22(7) (2017) 05017005.
[9] F.E. Hicks, Hydraulic flood routing with minimal channel data: Peace River, Canada, Canadian Journal of Civil Engineering, 23(2) (1996) 524-535.
[10] A.C. Fassoni-Andrade, F.M. Fan, W. Collischonn, A.C. Fassoni, R.C.D.d. Paiva, Comparison of numerical schemes of river flood routing with an inertial approximation of the Saint Venant equations, Rbrh, 23 (2018) e10.
[11] M. Roohi, K. Soleymani, M. Salimi, M. Heidari, Numerical evaluation of the general flow hydraulics and estimation of the river plain by solving the Saint–Venant equation, Modeling Earth Systems and Environment, 6 (2020) 645-658.
[12] D.M. Ferreira, C.V.S. Fernandes, J. Gomes, Verification of Saint-Venant equations solution based on the lax diffusive method for flow routing in natural channels, RBRH, 22(00) (2017) e25.
[13] H. Orouji, O. Bozorg Haddad, E. Fallah-Mehdipour, M.A. Mariño, Flood routing in branched river by genetic programming, in:  Proceedings of the Institution of Civil Engineers-Water Management, Thomas Telford Ltd, 2014, pp. 115-123.
[14] F. Zhao, T.I. Veldkamp, K. Frieler, J. Schewe, S. Ostberg, S. Willner, B. Schauberger, S.N. Gosling, H.M. Schmied, F.T. Portmann, The critical role of the routing scheme in simulating peak river discharge in global hydrological models, Environmental Research Letters, 12(7) (2017) 075003.
[15] Q. Si-min, B. Wei-min, S. Peng, Y. Zhongbo, J. Peng, Water-stage forecasting in a multi tributary tidal river using a bidirectional Muskingum method, Journal of Hydrologic Engineering, 14(12) (2009) 1299-1308.
[16] J. Chabokpour, A. Samadi, Analytical solution of reactive hybrid cells in series (HCIS) model for pollution transport through the rivers, Hydrological Sciences Journal, 65(14) (2020) 2499-2507.
[17] J. Chabokpour, Determination of flow parameters in layered rockfill media using tracer technique, Water Harvesting Research,  (2024).
[18] A.D. Koussis, K. Mazi, Reverse flood and pollution routing with the lag-and-route model, Hydrological Sciences Journal, 61(10) (2016) 1952-1966.
[19] P.K. Paul, N. Kumari, N. Panigrahi, A. Mishra, R. Singh, Implementation of cell-to-cell routing scheme in a large scale conceptual hydrological model, Environmental Modelling & Software, 101 (2018) 23-33.
[20] S. Farzin, V.P. Singh, H. Karami, N. Farahani, M. Ehteram, O. Kisi, M.F. Allawi, N.S. Mohd, A. El-Shafie, Flood routing in river reaches using a three-parameter Muskingum model coupled with an improved bat algorithm, Water, 10(9) (2018) 1130.
[21] M.H. Tewolde, J. Smithers, Flood routing in ungauged catchments using Muskingum methods, Water Sa, 32(3) (2006) 379-388.
[22] S. Zhang, L. Kang, L. Zhou, X. Guo, A new modified nonlinear Muskingum model and its parameter estimation using the adaptive genetic algorithm, Hydrology Research, 48(1) (2016) 17-27.
[23] V. Atashi, R. Barati, Y.H. Lim, Improved river flood routing with spatially variable exponent Muskingum model and sine cosine optimization algorithm, Environmental Processes, 10(3) (2023) 42.
[24] R.K. Price, An optimized routing model for flood forecasting, Water resources research, 45(2) (2009).
[25] M. Perumal, T. Moramarco, S. Barbetta, F. Melone, B. Sahoo, Real-time flood stage forecasting by Variable Parameter Muskingum Stage hydrograph routing method, Hydrology Research, 42(2-3) (2011) 150-161.
[26] T. AO, K. TAKEUCHI, H. ISHIDAIRA, On problems and solutions of the Muskingum-Cunge routing method applied to a distributed rainfall runoff model, PROCEEDINGS OF HYDRAULIC ENGINEERING, 44 (2000) 139-144.
[27] G. Tayfur, V.P. Singh, T. Moramarco, S. Barbetta, Flood hydrograph prediction using machine learning methods, Water, 10(8) (2018) 968.
[28] A. Ghumman, U. Ghani, M. Shamim, Flood forecasting using neural networks, in:  International Workshop on Artificial Neural Networks: data preparation techniques and application development, SCITEPRESS, 2004, pp. 9-15.
[29] P.R. Wormleaton, M. Karmegam, Parameter optimization in flood routing, Journal of hydraulic engineering, 110(12) (1984) 1799-1814.
[30] V. Kumar, H.M. Azamathulla, K.V. Sharma, D.J. Mehta, K.T. Maharaj, The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management, Sustainability, 15(13) (2023) 10543.
[31] S. Zang, Z. Li, K. Zhang, C. Yao, Z. Liu, J. Wang, Y. Huang, S. Wang, Improving the flood prediction capability of the Xin’anjiang model by formulating a new physics-based routing framework and a key routing parameter estimation method, Journal of Hydrology, 603 (2021) 126867.
[32] H. Meresa, C. Murphy, R. Fealy, S. Golian, Uncertainties and their interaction in flood hazard assessment with climate change, Hydrology and Earth System Sciences, 25(9) (2021) 5237-5257.
[33] E.-I. Koutsovili, O. Tzoraki, N. Theodossiou, G.E. Tsekouras, Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach, ISPRS International Journal of Geo-Information, 12(11) (2023) 464.
[34] J. Li, G. Wu, Y. Zhang, W. Shi, Optimizing flood predictions by integrating LSTM and physical-based models with mixed historical and simulated data, Heliyon, 10(13) (2024).
AUT Journal of Civil Engineering
Volume 8, Issue 2
2024
Pages 109-126

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