caa a22 4500 605470685 CHVBK 20210128100329.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00500-014-1258-0 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1258-0 Artificial neural network weights optimization based on social-based algorithm to realize sediment over the river [Elektronische Daten] [Fatemeh Ramezani, Mehdi Nikoo, Mohammad Nikoo] Predictions of sediment load are required in a wide spectrum of problems such as design of the dead volume of a dam, sediment transport in the river, design of stable channels, and dredging needs. Researchers have used the regression between sediment concentration and water discharge. Such relationships are obtained through the application of regression analysis in many studies. Unfortunately, in the classical regression approach to determine sediment concentration-water discharge relationships, internal uncertainties are not taken explicitly into consideration. Therefore, researchers look for non-linear methods to estimate sediment load such as artificial neural network (ANN) methods to solve non-linear problems. The main purpose of this paper is to optimize the ANN connection weights with novel social-based algorithm (SBA) to realize the sediment in Maroon river. The SBA tries to capture several people in different types of countries. They try to reach high levels. The approach illustrated feed-forward neural network optimization for sediment estimation of four Maroon river stations in Iran, which was called FF-SBA. Three inputs were presented in each station: length of river, discharge and debit. Sediment parameter on the same station is measured as the output parameter. Results of optimization algorithms such as the genetic algorithm, particle swarm optimization and imperialist competitive algorithm were compared with the SBA results, and it was found that the FF-SBA model exhibited more capability, flexibility, and accuracy in sediment training, testing, and forecasting steps for the Maroon river in Iran. Springer-Verlag Berlin Heidelberg, 2014 Maroon river nationallicence Sediment nationallicence Length of river nationallicence Social-based algorithm (SBA) nationallicence Feed-forward back-propagation algorithm nationallicence Ramezani Fatemeh Computer Engineering-Artificial Intelligence, Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran aut Nikoo Mehdi SAMA Technical and Vocational Training college, Islamic Azad University, Ahvaz Branch, Ahvaz, Iran aut Nikoo Mohammad SAMA Technical and Vocational Training college, Islamic Azad University, Ahvaz Branch, Ahvaz, Iran aut Soft Computing Springer Berlin Heidelberg 19/2(2015-02-01), 375-387 1432-7643 19:2<375 2015 19 500 https://doi.org/10.1007/s00500-014-1258-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1258-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ramezani Fatemeh Computer Engineering-Artificial Intelligence, Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran aut NATIONALLICENCE 700 1- Nikoo Mehdi SAMA Technical and Vocational Training college, Islamic Azad University, Ahvaz Branch, Ahvaz, Iran aut NATIONALLICENCE 700 1- Nikoo Mohammad SAMA Technical and Vocational Training college, Islamic Azad University, Ahvaz Branch, Ahvaz, Iran aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/2(2015-02-01), 375-387 1432-7643 19:2<375 2015 19 500