Analysis of New Hybrid Artificial Intelligence Models in Estimating Flood Flow :Case Study: Kashkan Watershed

Roustami, Saeid; Shahinejad, Babak; Younesi, Hojatolah; Torabipoudeh, Hassan; Dehghani, Reza

doi:10.22034/hyd.2022.49807.1620

Document Type : پژوهشی

Authors

¹ Ph.D. Student in Hydraulic Structures, Faculty of Agriculture, Lorestan University

² Assistant Professor, Department of Water Engineering, Faculty of Agriculture, Lorestan University

³ Associate Professor of Water Engineering, University of Lorestan

https://doi.org/10.22034/hyd.2022.49807.1620

Abstract

Flood is one of the natural phenomena that causes a lot of human and financial losses in the world every year and creates many problems for the economic and social development of countries. Therefore, in order to reduce the damage, control and guidance of this phenomenon, estimating flood discharge and identifying the factors affecting it is very important. In this study, in order to estimate the flood discharge of Kashkan catchment located in Lorestan province, new hybrid artificial intelligence models including artificial neural network - innovative gunner, artificial neural network - black widow spider and artificial neural network - chicken crowding during the period 1300-1400 were used. To evaluate the simulation performance, statistical indices of determination coefficient (R2), absolute mean error (MAE), Nash-Sutcliffe productivity coefficient (NSE), bias percentage (PBIAS) were used. The results showed that hybrid artificial intelligence models improve the performance of the single model. The results showed that the artificial neural network- innovative gunner model has more accuracy and less error than other models. Overall, the results showed that the use of hybrid artificial intelligence models is effective in estimating flood discharge and can be considered as a suitable and rapid solution in water resources management.

Keywords

20.1001.1.23833254.1400.8.29.10.5

Main Subjects

Geomorphology

References

Adnan, R.M., Liang, Z., Heddam, S., Zounemat-Kermani, M., Kisi, O., Li, B. (2020). Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology, 586,371-388.

Aljarah, I., Ala’M, A.Z., Faris, H., Hassonah, M.A., Mirjalili, S., & Saadeh, H. (2018). Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognitive Computation, 10(3), 478-495.

Arora, S., & Anand, P. (2019). Chaotic grasshopper optimization algorithm for global optimization. Neural Computing and Applications, 31(8), 4385-4405.

Babaali, H.R., Dehghani, R. (2017). The prediction of the flood peak discharge using a wavelet neural network, Journal of Hydrogeomorphology, 4(11), 21-42.

Dehghani, R., Torabi, H. (2022). The effect of climate change on groundwater level and its prediction using modern meta- heuristic model. Groundwater for Sustainable Development, 16(4), 224-238, https://doi.org/10.1016/j.gsd.2021.100702

Dehghani, R., Torabi, H., Younesi, H., Shahinejad, B. (2020). Investigating the Application of Hybrid Support Vector Machine Models in Predicting River Flow of Karkhe Basin, Journal of Hydrogeomorphology, 7(22), 155-175.

Ghorbani, M.A., Deo, RC., Karimi, V., Yassen, ZM., Terzi, O, (2018). Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey, Stochastic Environmental Research and Risk Assessment, 32(6), 1683-1697.

Kilinc, H.C., Haznedar, B.(2022). A Hybrid Model for Streamflow Forecasting in the Basin of Euphrates. Water, 14(80), 2-15

Kisi, O., Karahan, M., and Sen, Z. (2006). River suspended sediment modeling using fuzzy logic approach, Hydrology of Process, 20(2), 4351-4362.

Malik, A., Tikhamarine, Y., Souag-Gamane, D., Kisi, O., & Pham, Q. B. (2020). Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction. Stochastic Environmental Research and Risk Assessment, 34(11), 1755-1773.

Meng, X., Liu, Y., Gao, X., & Zhang, H. (2014). A new bio-inspired algorithm: chicken swarm optimization. In International Conference in Swarm Intelligence, 8, 86-94

Nagy, H., Watanabe, K., and Hirano, M. (2002). Prediction of sediment load concentration in rivers using artificial neural network model, Journal of Hydraulics Engineering, 128(3), 558-559.

Nourani, V., Kisi, Ö., Komasi, M. (2011). Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology, 402(1–2), 41–59.

Nourani, V., Alami, MT., Aminfar, MH. (2009) .A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence, 22(2), 466–472.

Pijarski, P., & Kacejko, P. (2019). A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG), Engineering Optimization, 51(12), 2049-2068.

Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47.

Sebastian, P.A., & Peter, K.V. (2009). Spiders of India. Universities press.

Sigaroodi, S.K., Chen, Q., Ebrahimi, S., Nazari, A., Choobin, B. (2014). Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran. Hydrol. Earth Syst. Sci, 18, 1995–2006, doi:10.5194/hess-18-1995-2014.

Tokar, A., Johnson, P. (1999). Rainfall-Runoff Modeling Using Artificial Neural Networks. J Hydrol. Eng, 4(3), 232-239.

Zouache, D., Arby, Y. O., Nouioua, F., & Abdelaziz, F. B. (2019). Multi-objective chicken swarm optimization: A novel algorithm for solving multi-objective optimization problems. Computers & Industrial Engineering, 129, 377-391.

Analysis of New Hybrid Artificial Intelligence Models in Estimating Flood Flow :Case Study: Kashkan Watershed

References

References

Volume 8, Issue 29 - Serial Number 29March 2022Pages 201-187

Volume 8, Issue 29 - Serial Number 29
March 2022
Pages 201-187