
Babak Mohammadi
Doctoral student

Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation
Author
Summary, in English
River suspended sediment load (SSL) estimation is of importance in water resources engineering and hydrological modeling. In this study, a novel hybrid approach is recommended for SSL estimation in which multi-layer perceptron (MLP) is hybridized with particle swarm optimization (PSO) and then, integrated with differential evolution algorithm (DE) called as MLP-PSODE. The hybrid MLP-PSODE model is implemented to model the SSL of Mahabad river located at northwest of Iran. For the sake of examination of the MLP-PSODE model performance, several techniques including multi-layer perceptron (MLP), multi-layer perceptron integrated with particle swarm optimization (MLP-PSO), radial basis function (RBF) and support vector machine (SVM) are selected as benchmarks. For this purpose, five different scenarios are considered for the modeling. The results indicated that the new hybrid model of MLP-PSODE is successful in estimating SSL by considering single input of discharge (Q) with high accuracy as compared to its alternatives with RMSE = 1794.4 ton·day−1, MAPE = 41.50% and RRMSE = 107.09%, which were much lower than those of MLP based model with RMSE = 3133.7 ton·day−1, MAPE = 121.40% and RRMSE = 187.03%. The developed MLP-PSODE model, not only outperforms its counterparts in terms of accuracy in extreme values estimation, but also it is found as a parsimonious model that incorporates lower number of input parameters in its structure for SSL estimation.
Publishing year
2020
Language
English
Publication/Series
Catena
Volume
198
Document type
Journal article
Publisher
Elsevier
Topic
- Oceanography, Hydrology, Water Resources
Keywords
- Differential evolution algorithm
- Hybrid technique
- Mahabad river
- Multi-layer perceptron
- Particle swarm optimization
- Suspended sediment load
Status
Published
ISBN/ISSN/Other
- ISSN: 0341-8162