Browsing by Author "Moazenzadeh, Roozbeh"

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Citation - WoS: 94
Citation - Scopus: 104
Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation
(Elsevier B.V., 2021) Babak Mohammadi; Yiqing Guan; Roozbeh Moazenzadeh; Mir Jafar Sadegh Safari; Guan, Yiqing; Moazenzadeh, Roozbeh; Safari, Mir Jafar Sadegh; Mohammadi, Babak
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. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 29
Citation - Scopus: 37
Soil moisture estimation using novel bio-inspired soft computing approaches
(Taylor and Francis Ltd., 2022) Roozbeh Moazenzadeh; Babak Mohammadi; Mir Jafar Sadegh Safari; K. W. Chau; Moazenzadeh, Roozbeh; Chau, Kwok-wing; Safari, Mir Jafar Sadegh; Mohammadi, Babak
Soil moisture (SM) is of paramount importance in irrigation scheduling infiltration runoff and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA) krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature relative humidity wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008–2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68 MAPE = 0.04) and ANFIS (RMSE = 2.55 MAPE = 0.07) exhibited the best and worst performance in SM estimation respectively. All three hybrid models (ANFIS-WOA ANFIS-KHA and ANFIS-FA) improved SM estimates reducing RMSE by 34 28 and 27% relative to the base ANFIS model respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15–25) [25–35) and ≥35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69 1.89 and 1.55 in the three SM intervals respectively. From the perspective of under- or over-estimation of moisture values ANFIS-WOA (RMSE = 1.44 MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94 MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall all three hybrid models performed better in the underestimation set compared to overestimation set. © 2022 Elsevier B.V. All rights reserved.