A novel equation for longitudinal dispersion coefficient prediction based on the hybrid of SSMD and whale optimization algorithm

Memarzadeh, Rasoul; Zadeh, Hossein Ghayoumi; Dehghani, Majid; Riahi-Madvar, Hossien; Seifi, Akram; Mortazavi, Seyed Mostafa

doi:10.1016/j.scitotenv.2020.137007

Cited by 33 publications

(16 citation statements)

References 38 publications

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“…In this research, 70% and 30% of the whole dataset are selected automatically by SSMD as training and testing subsets, respectively. For more details on the SSMD method, one can refer to [6], [7], [39], [73]- [76]. Table 1 represents the statistical features of selected subsets.…”

Section: B Data Pre-processing By Ssmdmentioning

confidence: 99%

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Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

et al. 2020

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In this paper, a new hybrid model is proposed using Subset Selection by Maximum Dissimilarity (SSMD) and adaptive neuro-fuzzy inference system (ANFIS) hybridized with the firefly algorithm (FFA) to predict the longitudinal dispersion coefficient (K x). The proposed framework (ANFIS-FFA), combines the specific structures and strengths of both ANFIS and FFA approaches. The FFA is used to derive the optimum ANFIS parameters. The K x data set includes 503 cross-sectional data point from small to large rivers. For pre-processing of the data set, the SSMD method is used, which is superior to the classical trial and error method. The database covers a wide range of river width (0.2-867m), and depths (0.034-19.9 m). Fifteen different combinations of river width (B), depth (H), flow velocity (U) and shear velocity (U *) are implemented as inputs to create fifteen estimative models. The output of the ANFIS-FFA model is compared with the ANFIS and previously published equations to check the performance of the proposed model. The results show that the highest accuracy is attained by the M1 model, with all geometric and hydrodynamic parameters as input variables in comparison with ANFIS and previous equations. The R 2 value, RMSE, MAE and NSE for ANFIS-FFA model are 0.67, 113.14 m 2 /s, 48 m 2 /s, and 0.63 for proposed dimensional model, and 0.35, 874.5, 520.8, and 0.1 in non-dimensional ANFIS-FFA model, respectively. These values were 0.37, 463.34 m 2 /s, 85.69 m 2 /s, and −5.19 for dimensional ANFIS model, and 0.11, 3269.88, 1932.09 and −11.54 for non-dimensional ANFIS model, respectively. Overall, hybridization caused 81%, 75%, 76% improvement in R 2 , RMSE and MAE. In another contribution of the paper, by using the matrix form of developed ANFIS-FFA optimized parameters, a novel explicit calculation procedure for estimation of K x is derived. Based on the results, the proposed ANFIS-FFA model exhibits significant improvements than the classical ANFIS and highlights that optimizing by nature-inspired optimization algorithms plays a critical role in strengthening the ANFIS estimations generality. INDEX TERMS Longitudinal dispersion coefficient, ANFIS-FFA, maximum dissimilarity method, natural rivers, adaptive neuro-fuzzy inference system.

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Section: B Data Pre-processing By Ssmdmentioning

confidence: 99%

“…Because of these shortcomings, RDR was proposed by Memarzadeh et al [76] as a new index which is capable of using for all negative, positive values and the logarithmic base of DR remains and calculates the normalized error of estimated values as follows:…”

Section: E Evaluation Criteriamentioning

confidence: 99%

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

et al. 2020

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“…Also, scatter plot, box plot of prediction and observations and errors and error distribution are used to evaluate the performance of each algorithm in hydrological drought forecasting. Also, RDR index which proposed by Memarzadeh et al [47] was utilized for model performance evaluation.…”

Section: H Grasshopper Optimization Algorithmmentioning

confidence: 99%

Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks

et al. 2020

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Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40.

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“…In this paper, the GA algorithm was used to solve the GEP optimization problem [46][47][48][49][50][51][52][53][54][55]. The general trend of the GA algorithm for solving the GEP problem is shown in Figure 1.…”

Section: Ga Optimizationmentioning

confidence: 99%

Generation Expansion Planning in the Presence of Wind Power Plants Using a Genetic Algorithm Model

et al. 2020

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One of the essential aspects of power system planning is generation expansion planning (GEP). The purpose of GEP is to enhance construction planning and reduce the costs of installing different types of power plants. This paper proposes a method based on a genetic algorithm (GA) for GEP in the presence of wind power plants. Since it is desirable to integrate the maximum possible wind power production in GEP, the constraints for incorporating different levels of wind energy in power generation are investigated comprehensively. This will allow the maximum reasonable amount of wind penetration in the network to be obtained. Besides, due to the existence of different wind regimes, the penetration of strong and weak wind on GEP is assessed. The results show that the maximum utilization of wind power generation capacity could increase the exploitation of more robust wind regimes. Considering the growth of the wind farm industry and the cost reduction for building wind power plants, the sensitivity of GEP to the variations of this cost is investigated. The results further indicate that for a 10% reduction in the initial investment cost of wind power plants, the proposed model estimates that the overall cost will be minimized.

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A novel equation for longitudinal dispersion coefficient prediction based on the hybrid of SSMD and whale optimization algorithm

Cited by 33 publications

References 38 publications

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks

Generation Expansion Planning in the Presence of Wind Power Plants Using a Genetic Algorithm Model

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