Evaluation of intelligent prediction models towards precision of flood peak flows

Document Type : Original Article

Authors

1 Department of Irrigation & Reclamation Engineering, University of Tehran, Karaj, Iran.

2 Department of Water Science and Engineering, Ferdowsi University of Mashhad, Iran,

3 Department of Irrigation and Reclamation Engineering, University of Tehran

Abstract

Due to the lack of hydrological and meteorological stations, the use of data-based models is essential. Herein efficiency of Gene Expression Programming Models and Support Vector Machine are evaluated involving peak flood discharge prediction of Mahneshan-Angoran basin as a case study, central Iran. For this purpose, observational data of 36 annual maximum daily flow (1975-2011), corresponding rainfall and average monthly temperature of three stations including Mehrabad, Yangikand and Qarahgoni were used. The observed and predicted peak discharge flows in both models were compared based on the RMSE, explanatory coefficient (R^2) and Nash-Sutcliffe (NSE) criteria. The mean values of RMSE in the validation stage for the GEP model in Yingikand, Qarahgooni and Mehrabad stations are equal to 0.049, 0.080 and 0.050, respectively, and in the training stage are equal to 0.042, 0.060 and 0.047, the mean values of R^2 in the validation stage in above mentioned stations are equal to 0.88, 0.86 and 0.87, respectively, and for the training stage estimated equal to 0.89, 0.89 and 0.92. NSE values in the validation stage is equal to 0.75 for all three stations. However that is equal to 0.77, 0.76 and 0.80 for the mentioned stations in the training stage. Also, the RMSE values in the SVM model for the validation stage are equal to 0.042, 0.040, 0.054, respectively, and in the training stage are equal to 0.053, 0.064 and 0.044. R^2 values in the validation stage are equal to 0.66, 0.85 and 0.73. Also for the training stage are equal to 0.86, 0.88 and 0.91. NSE values for validation are equal to 0.56, 0.75 and 0.61 and for the training stage are equal to 0.71, 0.77 and 0.80. According to the evaluation criteria, the GEP model performed relatively better and this model is more suitable for predicting floods in Mahneshan-Angoran basin.

Keywords


احمدی، ف.، رادمنش، ف. و میر عباسی نجف‌آبادی، ر. 1393. مقایسه روش­های برنامه­ریزی ژنتیک و ماشین بردار پشتیبان در پیش­بینی جریان روزانه رودخانه ) مطالعه موردی: رودخانه باراندوز چای). مجله آب‌وخاک. 6 (28): 1162-1171. 
خسروی، م.، سلاجقه، ع.، مهدوی، م. و محسنی ساروی، م. 1391. پیش­بینی سیل با استفاده از شبکه عصبی مصنوعی و رگرسیون چند متغیره غیرخطی. نشریه مرتع و آبخیزداری، مجله منابع طبیعی ایران، 65 (3):341-439.
شعبانلو، س.، صدقی، خ.، ثقفیان، ب. و موسوی جهرمی، ح. 1387. پهنه­بندی سیلاب در شبکه رودخانه­های استان گلستان با استفاده از GIS. مجله پژوهش آب ایران. 3 (2): 11-22.
قاسمی، ع.، حاجی بابایی، ا. و شمسایی، ا. 1392. مدیریت سیلاب، سامانه پیش­بینی و هشدار سیل. کنفرانس ملی مدیریت سیلاب.
محرم پور، م.، محرابی ،ع. و کاتوزی، م. 1390. به‌کارگیری ماشین بردار پشتیبان SVM برای پیش­بینی دبی روزانه. چهارمین کنفرانس مدیریت منابع آب ایران.
Aytek, A. and Kisi, O. 2008. A genetic programming approach to suspended sediment modeling. Journal of Hydrology. 351: 288-298
rmatics Integration Platform for Regional Flood Inundation Warning Systems. Journal of Water. 11(1).
Dey P. and Das A. 2016. A utilization of GEP (gene expression programming) metamodel and PSO (particle swarm optimization) tool to predict and optimize the forced convection around a cylinder. Journal of energy. 19: 447-458.
Ferreira, C. 2001. Gene expression program-ming a new adaptive algorithm for solving problems. Complex Systems 13(2): 87-129.
Huang, W., Cao, Z., Huang, M., Duan, W., Ni, Y. and Yang, w. 2019.  A New Flash Flood Warning Scheme Based on Hydrodynamic Modelling. Journal of Water. 11(6).
Khatibi, R., Ghorbani, M.A., Hasanpourkashani, M., and Kisi, O. 2010. Comparison of three artificial intelligence techniques for discharge routing”. Journal of hydrology. 403(3-4): 201-212.
Lu X., Ju Y., Wu L., Fan. J., Zhang F., Li Z. 2018. Daily pan evaporation modeling from local and cross-station data using three tree-basedmachine learning models. Journal of Hydrology. 556: 668-684.
Modaresi, F., Araghinejad, S. and Ebrahimi, K. 2018. A comparative assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K- Nearest Neighbor Regression for monthly streamflow forecasting in linear and nonlinear conditions. Water Resources Management. 32(1): 243-258.
Pai, P.F. and Hong W.C. 2007. A recurrent support vector regression model in rainfall forecasting. Hydrological Process. 21:819-827.
Vapnik V.N. 1998. Statistical Learning Theory. Wiley, New York.