pesd2021152004

	PRESENT ENVIRONMENT AND SUSTAINABLE DEVELOPMENT Volume 15, Issue no.2/ 2021

	Title: Prediction of flood using optimized neural network with Gray wolf algorithm (Maroon River case study)
	Authors : Sajjad Amiri Doumari 1 1 Department of Mathematics and Computer science, Sirjan University of Technology, Sirjan,Iran; amiri@sirjantech.ac.ir E-mail : * Corresponding author: : amiri.sajjad@hotmail.com; amiri@sirjantech.ac.ir ; Tel: +98-913-340-3952 (F.L.)
	https://doi.org/10.15551/pesd2021152004
	VIEW FULL TEXT
	Abstract Floods, as one of the most frequent natural hazards, cause irreparable damage to infrastructure and agriculture, and housing every year. In order to avoid financial and human losses, the importance of flood forecasting seems inevitable. Considering that floods are caused by many natural and anthropogenic factors and also have limitations such as lack of complete information. In this study, artificial neural networks have been used as an efficient method for flood prediction. The neural network inputs include the Dubai River and the Eshel River, this data was collected over 8 Years from the Maroon River. The network used is a multilayer perceptron, also the neural network weights are optimized by the Gray wolf algorithm and the results are compared with other common methods. Analysis of the output results shows that the neural network with the Gray Wolf algorithm has better results than neural network and Genetic algorithms and the error of this method is 0.53%, which indicates high accuracy and precision for flood prediction compared to other evolutionary algorithms. This method is used to obtain the best amount of data for testing and training. As the results, the best rate is 80% for training and 20% for testing. Obtained results show the neural network error squares with 80% of the training data and 20% of the test data.

	References Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. (10.1016/j.heliyon. 2018. e00938) Aoulmi, Yamina, et al. "Highly accurate prediction model for daily runoff in semi-arid basin exploiting Metaheuristic learning algorithms." IEEE Access 9 (2021): 92500-92515. https:// doi. org/10.1109/ACCESS.2021.3092074 Adib, A., & Mahmoodi, A. (2017). Prediction of suspended sediment load using ANN GA conjunction model with Markov chain approach at flood conditions. KSCE Journal of Civil Engineering, 21(1), 447-457 . https:// doi. org/10.1007/s12205-016-0444-2 Adib, Arash, et al. "A new approach for suspended sediment load calculation based on generated flow discharge considering climate change." Water Supply (2021). https:// doi. org/10.2166/ws.2021.069 Akobeng, A. K. (2007). Understanding diagnostic tests 1: sensitivity, specificity and predictive values. Acta paediatrica, 96(3), 338-3. https:// doi. org/10.1111/j.1651-2227.2006.00180.x. Bakhtiari, Morteza; Almasi, Mohammad; Shahni Karamzadeh, Nima. (2010). Investigation of two artificial neural network learning algorithms in estimating suspended sediment load of river sediment. The first conference on applied research in Iranian water resources. Kermanshah: Kermanshah University of Technology. (In persian) Berz, G. (2000). Flood disasters: lessons from the past—worries for the future. Paper presented at the Proceedings of the Institution of Civil Engineers-Water and Maritime Engineering . Bozorg-Haddad, O., Zarezadeh-Mehrizi, M., Abdi-Dehkordi, M., Loáiciga, H. A., & Mariño, M. A. (2016). A self-tuning ANN model for simulation and forecasting of surface flows. Water Resources Management, 30(9), 2907-2929. https:// doi. org/10.1007/s11269-016-1301-2 Bui, D. T., Pradhan, B., Nampak, H., Bui, Q.-T., Tran, Q.-A., & Nguyen, Q.-P. (2016). Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. Journal of Hydrology, 540, 317-330. https:// doi. org/10.1016/j.jhydrol.2016.06.027 Daliran, Firoozeh. (2014). Evaluation of flood effects using HEC-FIA software in Qahroud watershed. Master Thesis, Isfahan University of Technology.(In Persian) Ehteram, Mohammad, et al. "Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction." Environmental Science and Pollution Research 28.2 (2021): 1596-1611. https:// doi. org/10.1007/s11356-020-10421-y Fast, Laurent. (2013). Basics of Neural Networks: Structure, Algorithms, and Applications. Translated by Weiss, Hadi; Mafakheri, Kobra; Bagheri Shooraki, Saeed. Tehran: Nas Publications, 488 pages. (In Persian) Heidari, E., Sobati, M.A. and Movahedirad, S., 2016. Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemometrics and intelligent laboratory systems, 155, pp.73-85. https:// doi. org/10.1016/j.chemolab.2016.03.031 Heydari, Ali; Emami, Kamran; Mirni, Mohammad Hussein; Taqi Khan, Shahindokht; Moradi Fallah, Shadi; Barkhordar, Mehrdad. (2005). Flood forecasting and warning. Tehran, National Committee for Irrigation and Drainage of Iran.(In Persian) Latt, Z. Z. (2015 .)Application of feedforward artificial neural network in Muskingum flood routing: a black-box forecasting approach for a natural river system. Water Resources Management, 29(14), 4995-5014. https:// doi. org/10.1007/s11269-015-1100-1 16- Melesse, A., Ahmad, S., McClain, M., Wang, X., & Lim, Y. (2011 .)Suspended sediment load prediction of river systems: An artificial neural network approach. Agricultural Water Management, 98(5), 855-866 https:// doi. org/10.1016/j.agwat.2010.12.012 Mirjalili, S., Mirjalili, S. M., Lewis, A. (2014). Grey Wolf Optimizer, Advances in engineering software, 69, 46-61. https:// doi. org/10.1016/j.advengsoft.2013.12.007 Ramezani, Farhad. (2011). Critique of meta-discovery methods in compositional optimization. Master Thesis. Faculty of Engineering, Nabi Akram University, Tabriz.(In Persian) Seyed Abbasi, Mohammad, Goran Orimi, Mehdi; Farid Hosseini, Ali; Sharifi, Mohammad Baqir. (2011). Investigation of Cprecip parameter capability in order to determine the effect of snow on the prediction of daily river discharge by neural network and fuzzy neural network. Scientific Journal of Agriculture. Issue 35.(In persian) Soomlek, C., Kaewchainam, N., Simano, T., & So-In, C. (2015). Using backpropagation neural networks for flood forecasting in PhraNakhon Si Ayutthaya, Thailand. Paper presented at the 2015 International Computer Science and Engineering Conference (ICSEC). https:// doi. org/10.1109/ICSEC.2015.7401424 Yadav, Arvind, Snehamoy Chatterjee, and Sk Md Equeenuddin. "Suspended sediment yield modeling in Mahanadi River, India by multi-objective optimization hybridizing artificial intelligence algorithms." International Journal of Sediment Research 36.1 (2021): 76-91. https:// doi. org/10.1016/j.ijsrc.2020.03.018 Zhang, Yu, and Yonghe Hao. "Loss prediction of mountain flood disaster in villages and towns based on rough set RBF neural network." Neural Computing and Applications (2021): 1-12. https:// doi. org/10.1007/s00521-021-05902-1

© 2007 Present Environment and Sustainable Development
ISSN 1843-5971 (Printed version) ISSN 2284 – 7820 (online version)

Home Editorial Board

Webdesign Adrian Ursu ©2008 UAIC