Modeling Daily River Flow Using Simulator Meta-Models (Case study: Gamasiab River)

Subject Areas : Water Resource Management

massoumeh zeinalie ¹ , mohammad reza golabi ² , mohammad hossein Niksokhan ³ , mohammad reza Sharifi ⁴

1 - Ph.D. Candidate Water Resources, Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
2 - PhD in Water Resources, Faculty of Water Science Engineering, Shahid Chamran University of Ahvaz, Iran. (Corresponding Author)
3 - Associate Professor, Faculty of Environment, University of Tehran, Iran.
4 - Assistant Professor, Faculty of Water Science Engineering, Shahid Chamran University of Ahvaz, Iran.

Received: 2017-12-30 Accepted : 2018-05-23 Published : 2020-06-21

Keywords: ANFIS Model, GEP model, BN model, Gamasiab, Flow Modeling,

Abstract :

Background and Aim: The aim is first to express the differences and identify three models, namely, Gene Expression Programming (GEP), Neural-Fuzzy Network (ANFIS), and Bayesian Network (BN), and compare them with each other. Furthermore, the research's central question is whether the superior simulation meta-modal in this study can be a suitable alternative to conceptual models in the conditions of lack of data and information. Methods: The data used for this study are the daily rainfall and flow data of the Gamasiab Nahavand River in 10 years from 2002 to 2012. For the prediction or simulation stage, the data of the blue year 2012-2011 have been used. Results: In the training phase and according to the coefficient of explanation and the square root of the mean squares error and the AIC criterion, it is observed that in all three models, both in the training phase and in the test phase, we see a minimal difference in the amount of these parameters. Moreover, all three models' results are close to each other with almost a minimal difference, and almost the relative superiority of the GEP model can be seen. Discussion & Conclusion: The results indicate that the simulator meta-model of gene expression has an excellent ability to simulate and predict the river's daily flow, this simulation meta-model can be a suitable alternative to models in the absence of data and information. Be conceptual. Also, the speed of implementation of the gene expression programming model was faster than other models and was able to provide results in a short time.

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