River flow prediction using hybrid PSOGSA algorithm based on feed-forward neural network
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River flow modeling plays an important role in water resources management. This research aims at developing a hybrid model that integrates the feed-forward neural network (FNN) with a hybrid algorithm of the particle swarm optimization and gravitational search algorithms (PSOGSA) to predict river flow. Fundamentally, as the precision of a FNN model is essentially dependent upon the assurance of its model parameters, this review utilizes the PSOGSA for ideal preparing of the FNN model and gives the likelihood of boosting the execution of FNN. For this purpose, monthly river flow time series from 1990 to 2016 for Garber station of the Turkey River located at Clayton County, Iowa, were used. The proposed FNN-PSOGSA was applied in monthly river flow data. The results indicate that the FNN-PSOGSA model improves the forecasting accuracy and is a feasible method in predicting the river flow.
KeywordsFeed-forward neural networks Gravitational search algorithm Hybrid model Particle swarm optimization River flow forecasting Turkey River
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Conflict of interest
All authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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