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Soft Computing
Erschienen in: Soft Computing 20/2019

29.11.2018 | Methodologies and Application

River flow prediction using hybrid PSOGSA algorithm based on feed-forward neural network

verfasst von: Sarita Gajbhiye Meshram, Mohmmmad Ali Ghorbani, Shahaboddin Shamshirband, Vahid Karimi, Chandrashekhar Meshram

Erschienen in: Soft Computing | Ausgabe 20/2019

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Abstract

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.
Vorheriger Artikel Toward recursive spherical harmonics-issued bi-filters: Part I: theoretical framework
Nächster Artikel Some novel intuitionistic fuzzy information fusion methods in decision making with interaction among attributes

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Metadaten
Titel
River flow prediction using hybrid PSOGSA algorithm based on feed-forward neural network
verfasst von
Sarita Gajbhiye Meshram
Mohmmmad Ali Ghorbani
Shahaboddin Shamshirband
Vahid Karimi
Chandrashekhar Meshram
Publikationsdatum
29.11.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 20/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3598-7

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