Efficiency of MIKE-NAM model for runoff modeling using artificial intelligence
Abstract
Introduction.
The ability of runoff modeling is an essential step in the hydrologic modeling process and therefore water balance studies, therefore, this study aims to verify the ability and reliability of the MIKE 11NAM program in modeling runoff, in the upper basin of Orontes River in Syria, with the use of artificial intelligence models to fill the gaps in runoff time series.
Materials and methods.
In this study, models of artificial neural networks and fuzzy inference models were used and they were compared with each other to determine the best model in order to fill the gaps in the surface runoff data at Al-Jawadiyah and Al-Amiri stations. Then the results were used in the modeling process using the MIKE 11 NAM program.
Results.
The results showed a high reliability of artificial intelligence models, whether neural networks or fuzzy inference models, with a relative preference for neural networks, and after using these results within the data required for modeling in the Mike program, it was found that there are large differences between the observed and simulated values due to the lack of existing data on the study area.
Conclusions.
This study recommends to continue research on the issue of hydrological modeling in case of lack of data and to compare between different models to find the best way to solve this problem.
About the Authors
Alaa Slieman
Moscow State University of Civil Engineering (National Research University) (MGSU)
Russian Federation
Russian Federation
postgraduate of the Department of Hydraulics and Hydraulic Engineering
Dmitry V. Kozlov
Moscow State University of Civil Engineering (National Research University) (MGSU)
Russian Federation
Russian Federation
Doctor of Technical Sciences, Professor, Head of the Department of Hydraulics and Hydraulic Engineering
- Scopus: 36787104800
- ResearcherID: B-4808-2016
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Review
For citations:
Slieman A., Kozlov D.V. Efficiency of MIKE-NAM model for runoff modeling using artificial intelligence. Construction: Science and Education. 2022;12(4):89-102. (In Russ.)
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