Abstract
Flood routing is one of the methods of flood forecasting in rivers to manage and control the flood. Today, the new technique of using the intelligent models is widely reported in various fields of science and engineering, particularly water resources. In this research, flood routing was studied using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. By using the bat algorithm and imperialist competitive algorithm (ICA), the structure of ANN models was optimized. This process was repeated for combining genetic algorithm and particle swarm optimization algorithm with the ANFIS model. Four input patterns were used for network training, which It−7, It−6, Qt−1, Qt−2 pattern was the best pattern for network input according to the evaluation test. Results of routing of 8 flood hydrographs (6 hydrographs for network training and 2 hydrographs for network testing) indicated that the ANN–ICA predicted the hydrograph volume, peak flow and flood time more accurately. The statistical analyses at the training stage were: RMSE = 0.33, MARE = 0.32, SI = 0.05, BIAS = 0.18 and at the testing stage were: RMSE = 0.3, MARE = 0.32, SI = 0.04, BIAS = 0.08. Also, according to the sensitivity analysis, It−6 has the highest impact on flood discharge. Finally, the flood hydrograph was predicted for a return period of 10,000 years.
Similar content being viewed by others
References
Aksoy H, Dahamshed A (2009) Artificial neural network models for forecasting monthly precipitation in Jordan. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-008-0267-x
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congr Evol Comput. https://doi.org/10.1109/CEC.2007.4425083
Barati R (2011) Parameter estimation of nonlinear Muskingum models using Nelder-Mead simplex algorithm. J Hydrol Eng 16(11):946–954
Barati R (2013) Application of excel solver for parameter estimation of the nonlinear Muskingum models. KSCE J Civil Eng 17(5):1139–1148
Barati R (2018) Discussion of “Application of Genetic Programming to flow routing in simple and compound channels” by Elahe Fallah-Mehdipour, Omid Bozorg-Haddad, Hossein Orouji, and Miguel A. Mariño. J Irrig Drain Eng 144(5):07018015
Barati R, Rahimi S, Akbari GH (2012) Analysis of dynamic wave model for flood routing in natural rivers. Water Sci Eng 5(3):243–258
Barati R, Akbari GH, Rahimi S (2013) Flood routing of an unmanaged river basin using Muskingum–Cunge model: field application and numerical experiments. Casp J Appl Sci Res 2(6):08
Birkland TA, Burby RJ, Conrad D, Cortner H, Michener WK (2003) River ecology and flood hazard mitigation. Nat Hazards Rev 4(1):46–54. https://doi.org/10.1061/(ASCE)1527-6988(2003)4:1(46)
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Bowden GJ, Maier HR, Dandy GC (2005) Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river. J Hydrol 301(1):93–107. https://doi.org/10.1016/j.jhydrol.2004.06.020
Bozorg-Haddad O, Karimirad I, Seifollahi-Aghmiuni S, Loáiciga HA (2014) Development and application of the bat algorithm for optimizing the operation of reservoir systems. J Water Resour Plan Manag 141(8):04014097. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000498
Brody SD, Zahran S, Maghelal P, Grover H, Highfield WE (2007) The rising costs of floods: examining the impact of planning and development decisions on property damage in Florida. J Am Plan As 73(3):330–345. https://doi.org/10.1080/01944360708977981
Chan NW (2012) Impacts of disasters and disasters risk management in Malaysia: the case of floods. In: Sawada Y, Oum S (eds) Economic and welfare impacts of disasters in East Asia and Policy responses. ERIA Research Project Report 2011-8, ERIA, Jakarta, pp 503–551. https://doi.org/10.1007/978-4-431-55022-8_12
Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230:244–257. https://doi.org/10.1016/S0022-1694(00)00214-6
Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall-runoff modeling. Hydrol Sci J 43(1):47–66
Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL (2006) Flood estimation at ungauged sites using artificial neural networks. J Hydrol 319(1–4):391–409. https://doi.org/10.1016/j.jhydrol.2005.07.032
Floater G, Bujak A, Hamill G, Lee M (2014) RAMSES Project, WP 5: development of a cost assessment framework for adaptation, D5.1: review of climate change losses and adaptation costs for case studies. Technological Development and Demonstration under Grant Agreement No. 308497 (Project RAMSES), 22 p
Garson GD (1991) Interpreting neural network connection weights. J Artif Intell Expert 6:47–51
Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. J Ecol Model 160:249–264
Goh ATC (1995) Back-propagation neural networks for modeling complex systems. J Artif Intel Eng 9:143–151
Govindaraju RS, Rao AR (2000) Artificial neural networks in hydrology. Kluwer Academic Publisher, Dordrecht
Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 14:1942–1948
Khatibi R, Ghorbani MA, Kashani MH, Kisi O (2011) Comparison of three artificial intelligence techniques for discharge routing. J Hydrol 403(3–4):201–212. https://doi.org/10.1016/j.jhydrol.2011.03.007
Kisi O (2004) River flow modeling using artificial neural network. ASCE J Hydrol Eng 9(1):60–63. https://doi.org/10.1061/(ASCE)1084-0699(2004)9:1(60)
Kisi O, Nia AM, Gosheh MG, Tajabadi MRJ, Ahmadi A (2012) Intermittent streamflow forecasting by using several data driven techniques. Water Resour Manage 26(2):457–474. https://doi.org/10.1007/s11269-011-9926-7
Kumar APS, Sudheer KP, Jain SK, Agarwal PK (2005) Rainfall-runoff modeling using artificial neural networks: comparison of network types. Hydrol Process 19:1277–1291. https://doi.org/10.1002/hyp.5581
Kuo RJ, Chen CH, Hwang YC (2001) An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets Syst 118(1):21–45. https://doi.org/10.1016/S0165-0114(98)00399-6
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15:101–123. https://doi.org/10.1016/S1364-8152(99)00007-9
Murthy KR, Raju MR, Rao GG (2010) Comparison between conventional, GA and PSO with respect to optimal capacitor placement in agricultural distribution system. In: 2010 Annual IEEE India conference (INDICON), pp 1–4. doi:https://doi.org/10.1109/INDCON.2010.5712664
Mutlu E, Chaubey I, Hexmoor H, Bajwa SG (2008) Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrol Process 22(26):5097–5106. https://doi.org/10.1002/hyp.7136
Nguyen PKT, Chua LHC (2012) The data-driven approach as an operational real-time flood forecasting model. Hydrol Process 26:2878–2893. https://doi.org/10.1002/hyp.8347
Nikoo M, Ramezani F, Hadzima-Nyarko M, Nyarko EK, Nikoo M (2016) Flood-routing modeling with neural network optimized by social-based algorithm. Nat Hazards 82(1):1–24. https://doi.org/10.1007/s11069-016-2176-5
Nourani V, Kisi O, Komasi M (2011) Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. J Hydrol 402(1):41–59. https://doi.org/10.1016/j.jhydrol.2011.03.002
Pham DT, Koc E, Ghanbarzadeh A, Otri S (2006) Optimization of the weights of multi-layered perceptrons using the bees algorithm. In: Proceedings of 5th international symposium on intelligent manufacturing systems, pp 38–46
Ramezani F, Lotfi S (2013) Social-based algorithm (SBA). Appl Softw Comput 13(5):2837–2856. https://doi.org/10.1016/j.asoc.2012.05.018
Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386
Salimi A, Karami H, Farzin S, Hassanvand M, Azad A, Kisi O (2018) Design of water supply system from rivers using artificial intelligence to model water hammer. ISH J Hydraul Eng. https://doi.org/10.1080/09715010.2018.1465366
Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress evolutionary computation, CEC 99, vol 3, pp 1945–1950. IEEE. doi:https://doi.org/10.1109/CEC.1999.785511
Sivanandam SN, Deepa SN (2007) Introduction to genetic algorithms. Springer, Berlin
Sudheer KP, Gosain AK, Ramasastri KS (2002) A data driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16(6):1325–1330. https://doi.org/10.1002/hyp.554
Wright JM (2000) A report by the Association of State Floodplain Managers. The Nation’s responses to flood disasters: a historical account, Association of State Floodplain Managers, Madison
Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Gonzalez JR et al (eds) Nature inspired cooperative strategies for optimization (NISCO 2010), vol 284. Springer, Berlin, pp 65–74
Zadeh MR, Amin S, Khalili D, Singh VP (2010) Daily outflow prediction by multilayer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manag 24(11):2673–2688. https://doi.org/10.1007/s11269-009-9573-4
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hassanvand, M.R., Karami, H. & Mousavi, SF. Investigation of neural network and fuzzy inference neural network and their optimization using meta-algorithms in river flood routing. Nat Hazards 94, 1057–1080 (2018). https://doi.org/10.1007/s11069-018-3456-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-018-3456-z