Investigation of the Efficiency of Group Method of Data Handeling and Wavelet Transform in Runoff Forecasting (Case Study: Gharehsoo Watershed)

Authors

1 Assistant Professor, Department of Civil Engineering, Yazd University, Yazd, Iran

2 M.Sc. Student of Civil Engineering - Water and Hydraulic Structures, Grand Ayatollah Borujerdi

Abstract

Rainfall-runoff process is one of the most important and complex phenomena in the hydrological cycle. Therefore, in its modeling, different perspectives for the development and improvement of predictive models have been presented. In this study, while introducing a combination of wavelet-group classification of data, its effectiveness for modeling the rainfall-run-off process in the Ghara-eos watershed was studied. At first, the rainfall and runoff time series were decomposed using a wavelet transform to several sub-basins to overcome its non-state. Then, these time subcircuits are considered as inputs of the grouped data collection method for predicting daily runoff. The efficiency of the combined model with DC and root mean square error (RMSE) were evaluated. The results of the validation of the models indicate that the highest amount of explanation coefficient and the lowest root mean of error for the single GMDH model were 0.65 and 0.07, respectively, and for the combined model of grouping the wavelet data The order is 0.91 and 0.05. The reason for the hybrid model's superiority to the single model is that the combination model of the grouping of wavelet data, instead of using the time series of rainfall and runoff data on a general scale, uses several time- Different decompositions are used as inputs in the model. Also, the results showed that the Wavelet-GMDH combination model compared to other composite models such as Waveline Artificial Neural Network (WANN) due to the GMDH model layer function, which includes binary combinations of input variables, and by selecting the number of optimal neurons in Each layer directs motion to the predicted data, has more efficiency and accuracy.

Keywords


ایوانی، ز.، احمدی، م.، قادری، ک.، 1395، برآورد بار معلق رودخانه­ای با استفاده از روش گروهی کنترل داده­ها، پژوهشنامه مدیریت حوزه آبخیز، (13)2: 61-71.
پورحقی، ا.، سلگی، ا.، رائمنش، ف.، شهنی دارابی، م.، 1397، استفاده ترکیبی از تبدیل موجک و مدل‌های هوشمند در شبیه‌سازی جریان رودخانه (مطالعه موردی: رودخانه‌های کاکارضا و سراب صیدعلی)، نشریه علمی پژوهشی مهندسی آبیاری و آب ایران، شماره 32، 17-1.
دوانلوتاجبخش، ع.، نورانی، و.، مولاجو، ا.، 1398، بررسی کارایی مدل هیبریدی Wavelet-M5 در پیش­بینی فرآیند بارش-رواناب(مطالعه موردی: حوضه آجی­چای)، نشریه تحقیقات منابع آب، شماره15، 10-1.
کماسی، م.، شرقی، س.، 1396، روندیابی عوامل موثر بر کاهش تراز آب زیرزمینی با بهره­گیری از تبدیلات موجک متقابل و ارتباطی، نشریه علمی پژوهشی مهندسی آبیاری و آب ایران، شماره 28، 151-138.
کماسی، م.، نوذری، ح،. قشلاقی، ن.، 1395، پیش­بینی تراز آب دریاچه ارومیه با استفاده از روش­های سری زمانی، شبکه عصبی مصنوعی و شبکه عصبی-موجکی، نشریه علمی پژوهشی مهندسی آبیاری و آب ایران، شماره 24، 77-64.
Hsu, K.L., Gupta, H.V. and Sorooshian, S., 1995. Artificialneural network modeling of the rainfall‐runoff process. Water resources research31(10), pp.2517-2530.
Ivakhnenko, A.G., 1968. The Group Method of Data of Handling; A rival of the method of stochastic approximation. Soviet Automatic Control13, pp.43-55.
Nakken, M., 1999. Wavelet analysis of rainfall–runoff variability isolating climatic from anthropogenic patterns. Environmental Modelling & Software14(4), pp.283-295.
Nikolaev, N.Y. and Iba, H., 2003. Polynomial harmonic GMDH learning networks for time series modeling. Neural Networks16(10), pp.1527-1540.
a) Nourani, V., Alami, M.T. and Aminfar, M.H., 2009. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence, 22(3), pp.466-472.
b)  Nourani, V., Komasi, M. and Mano, A., 2009. A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water resources management23(14), p.2877.
Nourani, V., Baghanam, A.H., Adamowski, J. and Kisi, O., 2014. Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, pp.358-377.
Santin, I., 2015. Effluent Predictions in Wastewater Treatment Plants for the Control Strategies Selection, Journal of Bilbao, 2: 1009-1016.
Singh, V.P., 1989. Hydrologic systems: watershed modeling. Prentice Hall, University of California, 320p
Zhang, H., Liu, X., Cai, E., Huang, G. and Ding, C., 2013. Integration of dynamic rainfall data with environmental factors to forecast debris flow using an improved GMDH model. Computers & geosciences, 56, pp.23-31.
Zahabiyoun, B., Goodarzi, M.R., Bavani, A.M. and Azamathulla, H.M., 2013. Assessment of climate change impact on the Gharesou River Basin using SWAT hydrological model. CLEAN–Soil, Air, Water41(6), pp.601-609.