Flood-routing modeling with neural network optimized by social-based algorithm

Mehdi Nikoo (), Fatemeh Ramezani (), Marijana Hadzima-Nyarko (), Emmanuel Karlo Nyarko () and Mohammad Nikoo ()
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Mehdi Nikoo: Islamic Azad University
Fatemeh Ramezani: Islamic Azad University
Marijana Hadzima-Nyarko: University of J.J. Strossmayer
Emmanuel Karlo Nyarko: University J.J. Strossmayer in Osijek
Mohammad Nikoo: Islamic Azad University , Ahvaz Branch

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2016, vol. 82, issue 1, No 1, 24 pages

Abstract: Abstract Forecasting and operational routing flood requires accurate forecasts on proper feed time, to be able to issue suitable warnings and take suitable emergency actions. Flood-routing problem is one of the most complicated matters in hydraulics of open channels and river engineering. Flood routing is the process of computing the progressive time and shape of a flood wave at successive points along a river. To get an approximate solution of the flood-routing problem, different techniques are used. This paper describes an approach to train artificial neural network (ANN) using social-based algorithm (SBA). The approach illustrates feed-forward neural network optimization for the flood-routing problem of Kheir Abad River called FF-SBA. To this end, the number and effective time lag of input data in ANN models are initially determined by means of linear correlation between input and output time series; subsequently, the weights of the feed-forward network is optimized by SBA. Optimization algorithms and statistical models like Genetic Algorithm and linear regression are compared to FF-SBA. Compared to the results of optimization algorithms and statistical models, the FF-SBA model for the Kheir Abad River in Iran shows more flexibility and accuracy.

Keywords: Routing river flood; Time series; Feed-forward neural network; Social-based algorithm (SBA) (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11069-016-2176-5

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