Flood routing modeling with neural network optimized by social based algorithm
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.
Natural Hazards