Artificial intelligence simulation of suspended sediment load with different membership functions of ANFIS
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ORIGINAL ARTICLE
Artificial intelligence simulation of suspended sediment load with different membership functions of ANFIS Meisam Babanezhad1,2 • Iman Behroyan3 • Azam Marjani4,5 • Saeed Shirazian6,7 Received: 25 March 2020 / Accepted: 26 October 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Modeling suspended sediment load is a critical element of water resources engineering. In this work, using the ANFIS method, everyday suspended sediment particles were estimated in different categories of the river in US Sediment big data, and various flow rates were utilized for testing and training. The artificial intelligent (AI) method called ANFIS is used to train actual data from the river and provide an AI model with artificial data points. This artificial data point can show the occurrence of disaster for a critical day with different flow rates. The changing parameter in the AI model enables us to make a correct decision about critical time for rivers. This study also concentrates on the sensitivity investigation of ANFIS setting parameters on the accurateness of numerical results in order to find the best ANFIS model for rapid oscillation in the data set. The best performance of the ANFIS method is achieved with the trimf membership function, the number of input membership function = 16, and the number of iteration = 1000. The results also showed that the ANFIS model can provide fast computational calculation, and adding more nodes for the prediction cannot change the overall time of calculation due to the meshless behavior of the model. In addition to this model, we used the ant colony method for training of data set, and we found that the ANFIS method is better in learning and prediction of the dataset. Keywords Artificial intelligence ANFIS Numerical study Prediction
1 Introduction
& Azam Marjani [email protected] 1
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
2
Faculty of Electrical and Electronic Engineering, Duy Tan University, Da Nang 550000, Vietnam
3
Faculty of Mechanical and Energy Engineering Department, Shahid Beheshti University, Tehran, Iran
4
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
5
Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam
6
Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland
7
Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin Prospekt, Chelyabinsk, Russia 454080
The accurate estimating of the sediment’s volume carried by large channels or rivers is significantly essential in water engineering and optimization of channel structure because of its direct influence on planning, designing, managing, and operating the hydraulic structures. To date, various attempts have been made to discover the association between the flow features and quantity of suspended load in the river,
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