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The aim of this study is to evaluate the ability of soft computing models including multilayer perceptron- (MLP-) water wave optimization (MLP-WWO), MLP-particle swarm optimization (MLP-PSO), and MLP-genetic algorithm (MLP-GA), to simulate the daily and monthly reference evapotranspiration (ET) at the Aidoghmoush basin (Iran). Principal component analysis (PCA) was used to find the best input combination including the lagged ETs. According to the results, the ET values with 1, 2, and 3 (days) lags as well as those with 1, 2, and 3 (months) lags were the most effective variables in the formation of the PCs. The total variance proportion of inputs and eigenvalues was used to identify the most important variables. The accuracy of the models was assessed based on multiple statistical indices such as the mean absolute error (MAE), Nash–Sutcliff efficiency (NSE), and percent bias (PBIAS). The results showed that the performance of hybrid MLP models was better than that of the standalone MLP. The findings confirmed that the MLP-WWO could precisely predict ET.
Fatemeh Sayyahi; Saeed Farzin; Hojat Karami. Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization. Complexity 2021, 2021, 1 -12.
AMA StyleFatemeh Sayyahi, Saeed Farzin, Hojat Karami. Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization. Complexity. 2021; 2021 ():1-12.
Chicago/Turabian StyleFatemeh Sayyahi; Saeed Farzin; Hojat Karami. 2021. "Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization." Complexity 2021, no. : 1-12.
In this study, a novel least square support vector machine (LSSVM) model integrated with gradient-based optimizer (GBO) algorithm is introduced for assessment of water quality parameters. For this purpose, three stations including Ahvaz, Armand, and Gotvand in the Karun river basin have been selected to model electrical conductivity (EC), and total dissolved solids (TDS). First, to prove the superiority of the LSSVM-GBO algorithm, the performance is evaluated with three benchmark datasets (Housing, LVST, Servo). Then, the results of the new hybrid algorithm were compared with those of artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and LSSVM algorithms. Input combination for assessment of water quality parameters EC and TDS consists of Ca+ 2, Cl-1, Mg+ 2, Na+ 1, SO4, HCO3, sodium absorption ratio (SAR), sum cation (Sum.C), sum anion (Sum.A), PH, and Q. The modelling results based on evaluation criteria showed the significant performance of LSSVM-GBO among all benchmark datasets and algorithms. Other results showed that in Ahvaz station, Sum.C, Sum.A, and Na+1 parameters, and in Gotvand and Armand stations, Sum.C, Sum.A, and Cl-1 parameters have the greatest impact on modelling EC and TDS parameters. In the next step, EC and TDS modelling was performed based on the best input combination and the best algorithm in different time delays. Based on the results, the highest accuracy of modelling EC and TDS parameters in Gotvand station was [0] month time delays.
Mojtaba Kadkhodazadeh; Saeed Farzin. A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters. 2021, 1 .
AMA StyleMojtaba Kadkhodazadeh, Saeed Farzin. A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters. . 2021; ():1.
Chicago/Turabian StyleMojtaba Kadkhodazadeh; Saeed Farzin. 2021. "A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters." , no. : 1.
Finding an optimal design for hydraulic structures and devices, which work together in irrigation networks, can be formulated as a multi-objective optimization problem. In this paper, a novel framework is proposed for simultaneous optimization of an open channel section and a labyrinth weir geometry. A recently proposed optimizer called Multi-Objective Multi-Verse Optimization (MOMVO) algorithm is employed and its results are compared with Pareto Envelope-based Selection Algorithm II (PESA-II) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) using five metrics, including spacing (SP), Maximum Spread (MS), Non-uniformity of Pareto Front (NPF), Mean Ideal Distance (MID), and Coverage Measure (CM). The first objective function is defined to minimize the construction costs per unit length of the open channel, and the second one is to minimize total concrete volume of the labyrinth weir. The results showed significant differences between MOMVO Pareto optimal solutions and the other two algorithms. The least values of SP, NPF, and MID metrics were provided by MOMVO, which meant its solutions had better conditions regarding uniformity and the closeness to the ideal point. To optimize irrigation network as a system, penalty functions were applied to satisfy hydraulic conditions (flow velocity, Froude number, and nappe interference). Results showed that if the proposed model had been employed in Isfahan Irrigation Networks (IINs) design, it would have reduced the construction costs of open channel and labyrinth weir approximately 11% and 74%, respectively. It can be reported that the most cost-effective design has the least channel wetted perimeter, channel cross-section top width, cross-sectional area, and labyrinth apex length; and the highest channel depth, and weir sidewall angles.
Ahmad Ferdowsi; Mahdi Valikhan-Anaraki; Sayed-Farhad Mousavi; Saeed Farzin; SeyedAli Mirjalili. Developing a model for multi-objective optimization of open channels and labyrinth weirs: Theory and application in Isfahan Irrigation Networks. Flow Measurement and Instrumentation 2021, 80, 101971 .
AMA StyleAhmad Ferdowsi, Mahdi Valikhan-Anaraki, Sayed-Farhad Mousavi, Saeed Farzin, SeyedAli Mirjalili. Developing a model for multi-objective optimization of open channels and labyrinth weirs: Theory and application in Isfahan Irrigation Networks. Flow Measurement and Instrumentation. 2021; 80 ():101971.
Chicago/Turabian StyleAhmad Ferdowsi; Mahdi Valikhan-Anaraki; Sayed-Farhad Mousavi; Saeed Farzin; SeyedAli Mirjalili. 2021. "Developing a model for multi-objective optimization of open channels and labyrinth weirs: Theory and application in Isfahan Irrigation Networks." Flow Measurement and Instrumentation 80, no. : 101971.
This study employs two heuristic algorithms, including the genetic algorithm (GA) and ant colony optimization for continuous domains (ACOR), for optimizing the parameters of two soft computing models, namely adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machine (LSSVM), which were used for modeling monthly precipitation for all 12 months of the year. Data from 40 meteorological stations situated in different parts of Iran were used. The effectiveness of input data was determined by internal correlation-coefficient and nonlinear sensitivity analysis. Selected input data were further evaluated by another sensitivity analysis method, cosine amplitude (CA). Considering different evaluation months, LSSVM was more accurate and reliable than ANFIS. It was also found that both algorithms improved the performance of models for most months of the year. ACOR was better and more reliable than was GA in optimizing the models. ACOR produced the best results in autumn that led to the improvement of performance of ANFIS in terms of correlation coefficient (R) and root-mean square error (RMSE) by 35% and 0.40 mm for October; 42% and 0.99 mm for November; and 31% and 0.74 mm for December. The performance of LSSVM was enhanced by 6% and 0.28 mm for October; 22% and 0.20 mm for November; and 4% and 0.10 mm for December, respectively. For July and August, the suggested algorithms could not improve the performance of ANFIS. The algorithms did optimize LSSVM in all months, so the RMSE and mean absolute error were improved by 0.15 and 0.28 mm for July and 0.28 and 0.56 mm for August, respectively.
Armin Azad; Saeed Farzin; Hadi Sanikhani; Hojat Karami; Ozgur Kisi; Vijay P. Singh. Approaches for Optimizing the Performance of Adaptive Neuro-Fuzzy Inference System and Least-Squares Support Vector Machine in Precipitation Modeling. Journal of Hydrologic Engineering 2021, 26, 04021010 .
AMA StyleArmin Azad, Saeed Farzin, Hadi Sanikhani, Hojat Karami, Ozgur Kisi, Vijay P. Singh. Approaches for Optimizing the Performance of Adaptive Neuro-Fuzzy Inference System and Least-Squares Support Vector Machine in Precipitation Modeling. Journal of Hydrologic Engineering. 2021; 26 (4):04021010.
Chicago/Turabian StyleArmin Azad; Saeed Farzin; Hadi Sanikhani; Hojat Karami; Ozgur Kisi; Vijay P. Singh. 2021. "Approaches for Optimizing the Performance of Adaptive Neuro-Fuzzy Inference System and Least-Squares Support Vector Machine in Precipitation Modeling." Journal of Hydrologic Engineering 26, no. 4: 04021010.
In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination optimization algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impacts of climate change on runoff and suspended sediment load (SSL) in the Lighvan Basin in the period (2020–2099). Also, the results of modeling were compared to those of LS-SVM and adaptive neuro-fuzzy inference system (ANFIS) methods. The comparison of runoff and SSL modeling results showed that the LS-SVM-FPA algorithm had the best results and the ANFIS algorithm had the worst results. After the acceptable performance of the LS-SVM-FPA algorithm was proved, the algorithm was used to predict runoff and SSL under climate change conditions based on ensemble GCM outputs for periods (2020–2034, 2035–2049, 2070–2084, and 2085–2099) under three scenarios of RCP2.6, RCP4.5, and RCP8.5. The results showed a decrease in the runoff in all periods and scenarios, except for the two near periods under the RCP2.6 scenario for runoff. The predicted runoff and SSL time series also showed that the SSL values were lower than the average observation period, except for 2036–2039 (up to an 8% increase in 2038).
Saeed Farzin; Mahdi Valikhan Anaraki. Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy. Journal of Water and Climate Change 2021, 1 .
AMA StyleSaeed Farzin, Mahdi Valikhan Anaraki. Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy. Journal of Water and Climate Change. 2021; ():1.
Chicago/Turabian StyleSaeed Farzin; Mahdi Valikhan Anaraki. 2021. "Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy." Journal of Water and Climate Change , no. : 1.
The present study aimed to propose a new optimization algorithm named Flow Direction Algorithm (FDA), which is a physics-based algorithm. The FDA algorithm mimics the flow direction to the outlet point with the lowest height in a drainage basin. In other words, flow moves to neighbor with lowest high or best objective function. Thirteen classic mathematical benchmark functions, ten new mathematical benchmark functions and five engineering design problems, including three-bar truss, tension/compression spring, speed reducer, gear train, and welded beam design, with different properties are considered to evaluate the proposed algorithm. Comparing the results of the FDA with other optimization algorithms demonstrates the superior performance of the FDA in solving challenging optimization problems.
Hojat Karami; Mahdi Valikhan Anaraki; Saeed Farzin; SeyedAli Mirjalili. Flow Direction Algorithm (FDA): A Novel Optimization Approach for Solving Optimization Problems. Computers & Industrial Engineering 2021, 156, 107224 .
AMA StyleHojat Karami, Mahdi Valikhan Anaraki, Saeed Farzin, SeyedAli Mirjalili. Flow Direction Algorithm (FDA): A Novel Optimization Approach for Solving Optimization Problems. Computers & Industrial Engineering. 2021; 156 ():107224.
Chicago/Turabian StyleHojat Karami; Mahdi Valikhan Anaraki; Saeed Farzin; SeyedAli Mirjalili. 2021. "Flow Direction Algorithm (FDA): A Novel Optimization Approach for Solving Optimization Problems." Computers & Industrial Engineering 156, no. : 107224.
In the present study, for the first time, a new framework is used by combining metaheuristic algorithms, decomposition and machine learning for flood frequency analysis under climate-change conditions and application of HadCM3 (A2 and B2 scenarios), CGCM3 (A2 and A1B scenarios) and CanESM2 (RCP2.6, RCP4.5 and RCP8.5 scenarios) in global climate models (GCM). In the proposed framework, Multivariate Adaptive Regression Splines (MARS) and M5 Model tree are used for classification of precipitation (wet and dry days), whale optimization algorithm (WOA) is considered for training least square support vector machine (LSSVM), wavelet transform (WT) is used for decomposition of precipitation and temperature, LSSVM-WOA, LSSVM, K nearest neighbor (KNN) and artificial neural network (ANN) are performed for downscaling precipitation and temperature, and discharge is simulated under present period (1972–2000), near future (2020–2040) and far future (2070–2100). Log normal distribution is used for flood frequency analysis. Furthermore, analysis of variance (ANOVA) and fuzzy method are employed for uncertainty analysis. Karun3 Basin, in southwest of Iran, is considered as a case study. Results indicated that MARS performed better than M5 model tree. In downscaling, ANN and LSSVM_WOA slightly outperformed other machine learning algorithms. Results of simulating the discharge showed superiority of LSSVM_WOA_WT algorithm (Nash-Sutcliffe efficiency (NSE) = 0.911). Results of flood frequency analysis revealed that 200-year discharge decreases for all scenarios, except CanESM2 RCP2.6 scenario, in the near future. In the near and far future periods, it is obvious from ANOVA uncertainty analysis that hydrological models are one of the most important sources of uncertainty. Based on the fuzzy uncertainty analysis, HadCM3 model has lower uncertainty in higher return periods (up to 60% lower than other models in 1000-year return period).
Mahdi Valikhan Anaraki; Saeed Farzin; Sayed-Farhad Mousavi; Hojat Karami. Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods. Water Resources Management 2020, 35, 199 -223.
AMA StyleMahdi Valikhan Anaraki, Saeed Farzin, Sayed-Farhad Mousavi, Hojat Karami. Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods. Water Resources Management. 2020; 35 (1):199-223.
Chicago/Turabian StyleMahdi Valikhan Anaraki; Saeed Farzin; Sayed-Farhad Mousavi; Hojat Karami. 2020. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods." Water Resources Management 35, no. 1: 199-223.
In this research, a new framework has been introduced for rainfall temporal variability evaluation by using combination of monthly rainfall data sets in three synoptic stations, Principal Component Analysis (PCA), Adaptive Neuro Fuzzy Inference System (ANFIS), Grasshopper Optimization Algorithm (GOA), and Innovative Trend Analysis (ITA) methodology. The first five components were chosen as inputs of the soft-computing models, based on PCA. The GOA was used for training the ANFIS model, in order to estimate the monthly rainfall. The outputs of the ANFIS-GOA were compared to the rainfall estimates by ANFIS-Particle Swarm Optimization (ANFIS-PSO) and ANFIS-Genetic Algorithm (ANFIS-GA). Moreover, various statistical indices, such as mean absolute error (MAE), percent bias (PBIAS) and Nash-Sutcliffe Efficiency (NSE) were used to evaluate the soft-computing models’ performance. Results indicated that ANFIS-GOA had higher accuracy in estimating the rainfall (values of MAE, NSE and PBIAS were 0.21, 0.92 and 0.16 for Mehrabad station, 0.16, 0.94 and 0.14 for Semnan station and 0.24, 0.91 and 0.17 for Noshahr station, respectively) in the testing phase. These values showed significant improvements (67.8%, 21% and 40% for Mehrabad station, 69.2%, 17.5% and 33.3% for Semnan station and 57.1%, 21.3% and 37% for Noshahr station) versus indices related to standalone ANFIS model, which reflected the supremacy and higher accuracy of ANFIS-GOA model in rainfall prediction for different climatic conditions. It was also concluded that the ANFIS-GOA, ANFIS-PSO, and ANFIS-GA models performed superior to the standalone ANFIS-based model, respectively. Furthermore, possible trends in monthly rainfall have been detected by ITA, which is a new graphical model. Results showed significant decreasing trends in January and July for all the rainfall values in Mehrabad station. By comparison of the results obtained from ANFIS and the hybrid models with observed data, it was also concluded that the trends of observed data were close to the ANFIS-GOA predictions.
Alireza Farrokhi; Saeed Farzin; Sayed-Farhad Mousavi. A New Framework for Evaluation of Rainfall Temporal Variability through Principal Component Analysis, Hybrid Adaptive Neuro-Fuzzy Inference System, and Innovative Trend Analysis Methodology. Water Resources Management 2020, 34, 3363 -3385.
AMA StyleAlireza Farrokhi, Saeed Farzin, Sayed-Farhad Mousavi. A New Framework for Evaluation of Rainfall Temporal Variability through Principal Component Analysis, Hybrid Adaptive Neuro-Fuzzy Inference System, and Innovative Trend Analysis Methodology. Water Resources Management. 2020; 34 (10):3363-3385.
Chicago/Turabian StyleAlireza Farrokhi; Saeed Farzin; Sayed-Farhad Mousavi. 2020. "A New Framework for Evaluation of Rainfall Temporal Variability through Principal Component Analysis, Hybrid Adaptive Neuro-Fuzzy Inference System, and Innovative Trend Analysis Methodology." Water Resources Management 34, no. 10: 3363-3385.
In the present study, different evolutionary methods, namely the bat algorithm (BA), particle swarm optimization (PSO) and their hybrid (HBP), are employed for the design of trapezoidal open-channel cross-sections with minimum construction cost (concrete lining plus excavation costs). For this purpose, considering open channels with uniform and composite roughness, fixed and variable freeboard, and also velocity, Froude number and flooding probability constraints, eight models were defined. The performance of HBP in terms of convergence rate was investigated using 10 random runs, and the resulting coefficient of variation for different models was 0.00001–0.002. Solutions of HBP were also compared to those of other optimization algorithms. The results indicated that using HBP, compared to BA, PSO, LINGO, Lagrange multiplier method and shuffled frog-leaping algorithm, led to a 32% saving in construction cost. Therefore, HBP has high potential for the optimal design of open channels.
Saeed Farzin; Mahdi Valikhan Anaraki. Optimal construction of an open channel by considering different conditions and uncertainty: application of evolutionary methods. Engineering Optimization 2020, 53, 1173 -1191.
AMA StyleSaeed Farzin, Mahdi Valikhan Anaraki. Optimal construction of an open channel by considering different conditions and uncertainty: application of evolutionary methods. Engineering Optimization. 2020; 53 (7):1173-1191.
Chicago/Turabian StyleSaeed Farzin; Mahdi Valikhan Anaraki. 2020. "Optimal construction of an open channel by considering different conditions and uncertainty: application of evolutionary methods." Engineering Optimization 53, no. 7: 1173-1191.
The present research introduces a model to find the best shape of a dam's spillway under climate change impacts, considering a benchmark problem (i.e., Ute Dam's labyrinth spillway in the Canadian River watershed, New Mexico, USA). A spillway design is based not only on historical data but also on the future hydrologic events. Climate variables were predicted for the years 2021–2050 based on three representative concentration pathway (RCP2.6, RCP4.5, and RCP8.5) scenarios of the general circulation model from the fifth phase of the coupled model intercomparison project (CMIP5) using the statistical downscaling model. Streamflow at the USGS 07226500 streamgage was simulated by a rainfall–runoff model with predicted data. Instantaneous peak flow was estimated using an empirical method. Flood frequency analysis was used for the estimation of the design flood. The shuffled frog-leaping algorithm (SFLA) is used to optimize a labyrinth spillway design and its results were compared with two other nature-inspired algorithms: invasive weed optimization (IWO) and cuckoo search (CS). The spillway was optimized once with the actual design flood (16,143 m3/s) and again with the design flood under climate change (12,250 m3/s). Results revealed that optimization with realistic design flood reduced the concrete volume of the spillway by 37% and under climate change by 43% using the SFLA.
Ahmad Ferdowsi; Sayed-Farhad Mousavi; Saeed Farzin; Hojat Karami. Optimization of dam's spillway design under climate change conditions. Journal of Hydroinformatics 2020, 22, 916 -936.
AMA StyleAhmad Ferdowsi, Sayed-Farhad Mousavi, Saeed Farzin, Hojat Karami. Optimization of dam's spillway design under climate change conditions. Journal of Hydroinformatics. 2020; 22 (4):916-936.
Chicago/Turabian StyleAhmad Ferdowsi; Sayed-Farhad Mousavi; Saeed Farzin; Hojat Karami. 2020. "Optimization of dam's spillway design under climate change conditions." Journal of Hydroinformatics 22, no. 4: 916-936.
Municipal and industrial wastewaters are serious threats for surface water and groundwater resources, and this threat can be converted to an opportunity by enhancing their quality, which then can be used for agricultural and landscape purposes. In the present study, the performance of porous concrete (PC) containing mineral adsorbents was investigated to improve the municipal wastewater quality. Firstly, the performance of adding fine grains (2.36–4.75 mm) in different portions (0, 10, and 20 % w/w of coarse aggregates) as well as mineral adsorbents (0.6–1.2 mm), namely zeolite, pumice, perlite, and LECA, in different portions (0, 5, 10 and 15 % w/w of coarse aggregates) on compressive strength, porosity and permeability coefficient of porous concrete was pursued. After evaluating these parameters, three samples from each percentage of fine-grains that had the highest compressive strength were selected for wastewater qualitative tests due to the fact that there was no significant difference between the other two factors. The experimental setup was next to the wastewater treatment plant of Semnan University, Semnan, Iran, which included a 200-L barrel and six canals to perform the quality tests. Six 100 × 100 × 100 mm PC specimens were positioned in each canal, in a zigzag pattern, to slow down the wastewater flow through the specimens. Inlet discharge for each canal was fixed at 0.5 L/min and total test time was 31.2 h. Qualitative parameters such as total suspended solids (TSS), chemical oxygen demand (COD), biochemical oxygen demand (BOD), and turbidity were measured before and after running the experiment. Results revealed that adding fine grains and the adsorbents increased the compressive strength, while they reduced the permeability coefficient and porosity. Also, the specimens containing zeolite and pumice had the highest compressive strength and permeability coefficient, respectively. Furthermore, mineral adsorbents reduced average concentration of TSS, BOD and COD by 40 %, 48 % and 30.5 %, respectively. The only factor that affected average turbidity level (49 NTU) was porosity, and not the mineral adsorbents. Finally, zeolite had the highest physical characteristics as well as high pollutant-reduction potential. However, it is recommended to perform further tests on different porous concrete mixtures and other adsorbents.
Ehsan Teymouri; Sayed-Farhad Mousavi; Hojat Karami; Saeed Farzin; Maryam Hosseini Kheirabad. Municipal Wastewater pretreatment using porous concrete containing fine-grained mineral adsorbents. Journal of Water Process Engineering 2020, 36, 101346 .
AMA StyleEhsan Teymouri, Sayed-Farhad Mousavi, Hojat Karami, Saeed Farzin, Maryam Hosseini Kheirabad. Municipal Wastewater pretreatment using porous concrete containing fine-grained mineral adsorbents. Journal of Water Process Engineering. 2020; 36 ():101346.
Chicago/Turabian StyleEhsan Teymouri; Sayed-Farhad Mousavi; Hojat Karami; Saeed Farzin; Maryam Hosseini Kheirabad. 2020. "Municipal Wastewater pretreatment using porous concrete containing fine-grained mineral adsorbents." Journal of Water Process Engineering 36, no. : 101346.
The contamination of waters by persistent organic pollutants, especially pharmaceutical contaminants, is one of the concerns all over the world. To date, among the treatment methods, the efficient EAOPs method have shown a high ability to treat this type of pollutant. However, conducting adequate tests for taking into account almost all possible conditions to predict the amount of pollutant removal in different conditions is still a challenge. On the other hand, achieving this aim requires a lot of cost and time. The superiority of data mining based methods over conventional mathematical methods have made these methods a good solution to solve this problem. Hence, in present study a model by employing data mining algorithms includes Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), M5 model tree, least-square support vector machine (LSSVM) and hybrid of LSSVM and firefly optimization algorithm (FFA), scatter interpolation method, and multi-criteria decision making, namely DID, is presented for modeling of drugs removal. For this purpose, four different inputs include current density, electrolyte concentration, pH, and electrolysis time are used for electrochemical removal of Ciprofloxacin (CIP) as a model pollutant. Subsequently, Scatter interpolation method is used for generating enough data for more accurate modeling and more reliable results. In the final part of the survey, the TOPSIS method under six scenario, is employed for ranking of algorithms by considering accuracy and time criteria. In defined scenarios for TOPSIS, six different weights are considered for time criteria as well as the weights of accuracy are considered as equal in each scenario. Also, the sum of scores of each algorithm in all scenarios is used for final decision. The finding results by TOPSIS for original data showed the superiority of LSSVM_FFA. After generating new data, the M5 and the ANFIS have better results in 0.25 time weight. However, by decreasing time weight and increasing accuracy weight (after second scenario), the M5 and the LSSVM_FFA have better results. Besides, based on the sum of scores for new data, the M5 and the LSSVM_FFA have superiority. Finally, it can be concluded that M5 in about to 3 s, and LSSVM_FFA in about to 17 s lead to estimate the drug removal value with good accuracy, and without needing to high cost, and several months laboratory works. Therefore, the mentioned models can be used for different tasks, such as determining the optimal removal of drug, and investigating the impact of different parameters on drug removal process, without needing to each special experiment. Thus, for generating the large data set, the results of the present study are reliable.
Saeed Farzin; Farideh Nabizadeh Chianeh; Mahdi Valikhan Anaraki; Fatemeh Mahmoudian. Introducing a framework for modeling of drug electrochemical removal from wastewater based on data mining algorithms, scatter interpolation method, and multi criteria decision analysis (DID). Journal of Cleaner Production 2020, 266, 122075 .
AMA StyleSaeed Farzin, Farideh Nabizadeh Chianeh, Mahdi Valikhan Anaraki, Fatemeh Mahmoudian. Introducing a framework for modeling of drug electrochemical removal from wastewater based on data mining algorithms, scatter interpolation method, and multi criteria decision analysis (DID). Journal of Cleaner Production. 2020; 266 ():122075.
Chicago/Turabian StyleSaeed Farzin; Farideh Nabizadeh Chianeh; Mahdi Valikhan Anaraki; Fatemeh Mahmoudian. 2020. "Introducing a framework for modeling of drug electrochemical removal from wastewater based on data mining algorithms, scatter interpolation method, and multi criteria decision analysis (DID)." Journal of Cleaner Production 266, no. : 122075.
This study presents the adsorption of sulfate by clinoptilolite and magnetic nanotubes (MNT) from the Gamasiab river (Kermanshah, Iran) samples. The samples have been characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), and Fourier-transform infrared spectroscopy (FT-IR). The results showed that selective methods were implemented properly for nanoparticle preparation. During the process operating, time was considered as the most significant factor in sulfate removal. Moreover, adsorbent to pollutant ratios (D/C), and pH was selected as control variables. The design of the experiment was used to find the optimal conditions for the use of adsorbents. The optimum adsorption points were obtained for the MNT at pH 8.97 and D/C = 6.12 and the clinoptilolite at pH 10.68 and D/C = 22.07. The effect of pH on the adsorbents indicated that the adsorbent performance in the alkaline condition has the highest efficiency. Hence, for MNTs, increasing pH value increased the adsorbance amount. To investigate the effect of D/C, the rate of adsorption showed an ascending trend. In addition, the equilibrium data were defined by Langmuir and Freundlich isotherm models, respectively. Freundlich isotherm well described the process of adsorbing sulfate by clinoptilolite with a correlation coefficient of 0.918. While the Langmuir isotherm was consistent with the adsorption process of sulfate by MNT with a correlation coefficient of 0.9728. The efficiency of sulfate adsorption for clinoptilolite and MNT in the natural river samples was calculated 91.5% and 97.8%, respectively. The results showed the superiority of MNT adsorption capability in river water conditions.
Amir Hossein Salami; Hossein Bonakdari; Azam Akhbari; Ali Shamshiri; Seyed Farhad Mousavi; Saeed Farzin; Mohammad Reza Hassanvand; Amir Noori. Performance assessment of modified clinoptilolite and magnetic nanotubes on sulfate removal and potential application in natural river samples. Journal of Inclusion Phenomena and Macrocyclic Chemistry 2020, 97, 51 -63.
AMA StyleAmir Hossein Salami, Hossein Bonakdari, Azam Akhbari, Ali Shamshiri, Seyed Farhad Mousavi, Saeed Farzin, Mohammad Reza Hassanvand, Amir Noori. Performance assessment of modified clinoptilolite and magnetic nanotubes on sulfate removal and potential application in natural river samples. Journal of Inclusion Phenomena and Macrocyclic Chemistry. 2020; 97 (1-2):51-63.
Chicago/Turabian StyleAmir Hossein Salami; Hossein Bonakdari; Azam Akhbari; Ali Shamshiri; Seyed Farhad Mousavi; Saeed Farzin; Mohammad Reza Hassanvand; Amir Noori. 2020. "Performance assessment of modified clinoptilolite and magnetic nanotubes on sulfate removal and potential application in natural river samples." Journal of Inclusion Phenomena and Macrocyclic Chemistry 97, no. 1-2: 51-63.
Green porous concrete (GPC) is a porous concrete (PC) with minimum amount of cement. This study investigated the effects of zeolite and pumice, as cementitious materials, on the physical properties of PC and the enhancement of its ability to reduce urban and industrial runoff pollution. GPC specimens were prepared by replacing cement with zeolite and pumice in different portions (10–40%). Results indicated that pumice had a better physical performance but zeolite had a greater ability to enhance GPC performance in improving wastewater quality. Furthermore, zeolite and pumice reduced the apparent density of concrete up to 181 and 92 kg/m3, respectively. Also, GPC performed well in eliminating TSS and turbidity. The use of 40% zeolite improved the ability of PC to reduce chemical oxygen demand (COD), Zinc (Zn), Copper (Cu), Cadmium (Cd) and Lead (Pb) by 38.6, 99, 99, 99 and 99%, respectively. The reductions in these water quality parameters due to the addition of 40% pumice were 25.4, 98, 96, 99 and 99%, respectively.
Armin Azad; Amir Saeedian; Sayed-Farhad Mousavi; Hojat Karami; Saeed Farzin; Vijay P. Singh. Effect of zeolite and pumice powders on the environmental and physical characteristics of green concrete filters. Construction and Building Materials 2019, 240, 117931 .
AMA StyleArmin Azad, Amir Saeedian, Sayed-Farhad Mousavi, Hojat Karami, Saeed Farzin, Vijay P. Singh. Effect of zeolite and pumice powders on the environmental and physical characteristics of green concrete filters. Construction and Building Materials. 2019; 240 ():117931.
Chicago/Turabian StyleArmin Azad; Amir Saeedian; Sayed-Farhad Mousavi; Hojat Karami; Saeed Farzin; Vijay P. Singh. 2019. "Effect of zeolite and pumice powders on the environmental and physical characteristics of green concrete filters." Construction and Building Materials 240, no. : 117931.
River discharge is among the influential factors on the operation of water resources systems and the design of hydraulic structures, such as dams; so the study of it is of great importance. Several effective factors on this non-linear phenomenon have caused the discharge to be assumed as being accidental. According ...
M. Boustani; F. Mousavi; H. Karami; S. Farzin. Investigating the Chaotic Nature of Flow the Upstream and Downstream of Zayandehrud-Dam Reservoir Using Chaotic Systems’ Criteria. Journal of Water and Soil Science 2019, 23, 19 -32.
AMA StyleM. Boustani, F. Mousavi, H. Karami, S. Farzin. Investigating the Chaotic Nature of Flow the Upstream and Downstream of Zayandehrud-Dam Reservoir Using Chaotic Systems’ Criteria. Journal of Water and Soil Science. 2019; 23 (4):19-32.
Chicago/Turabian StyleM. Boustani; F. Mousavi; H. Karami; S. Farzin. 2019. "Investigating the Chaotic Nature of Flow the Upstream and Downstream of Zayandehrud-Dam Reservoir Using Chaotic Systems’ Criteria." Journal of Water and Soil Science 23, no. 4: 19-32.
In this research, conjunctive and integrated operation of surface and ground water resources of Behbahan plain (Maroon dam's reservoir and existing wells, respectively) was investigated. Simulation of allocation of water demands in this basin was performed by four scenarios, using WEAP software: 1) current conditions (M1), 2) reference scenario for ...
A. Kheyrandish; S. F. Mousavi; H. R. Ghafouri; S. Farzin. Conjunctive Use of Surface and Ground Water Resources by WEAP Simulation Model (A Case Study: Behbahan Plain, Khouzestan Province). Journal of Water and Soil Science 2019, 23, 125 -139.
AMA StyleA. Kheyrandish, S. F. Mousavi, H. R. Ghafouri, S. Farzin. Conjunctive Use of Surface and Ground Water Resources by WEAP Simulation Model (A Case Study: Behbahan Plain, Khouzestan Province). Journal of Water and Soil Science. 2019; 23 (4):125-139.
Chicago/Turabian StyleA. Kheyrandish; S. F. Mousavi; H. R. Ghafouri; S. Farzin. 2019. "Conjunctive Use of Surface and Ground Water Resources by WEAP Simulation Model (A Case Study: Behbahan Plain, Khouzestan Province)." Journal of Water and Soil Science 23, no. 4: 125-139.
Reservoirs’ optimal operation is a critical issue in the management of surface water resources. In the present study, after combining the whale optimization algorithm (WOA) with genetic algorithm (GA), which is called hybrid whale-genetic algorithm (HWGA), the precision and convergence rate of HWGA is evaluated in the optimal operation of continuous-time four-reservoir benchmark system (FRBS) and ten-reservoir benchmark system (TRBS). This combination benefits from the GA high precision and the WOA high convergence rate. The precision and convergence rate of HWGA, GA, and WOA are compared to the absolute optimum solution, obtained using Lingo software. Results indicated that the absolute optimal solution was 308.292 in the FRBS and 1194.441 in the TRBS. The best optimal solutions using the HWGA, GA and WOA were 96.08%, 95.76%, and 85.19% of the absolute optimal in the FRBS, respectively, and 97.24%, 89.54%, and 84.42% of the absolute optimal in the TRBS, respectively. So, the precision of HWGA, GA and WOA ranked first to third, respectively. Also, the variation coefficient of HWGA solutions (0.006 in the FRBS and 0.011 in the TRBS) had the lowest value in both benchmark systems. The variation coefficients of GA and WOA solutions were 1.24 and 3.57 times the variation coefficient of HWGA in the FRBS, respectively, and 1.55 and 5.32 times the variation coefficient of HWGA in the TRBS, respectively. Therefore, it could be concluded that in the current study the HWGA solutions variation range was narrower than other algorithms’ solutions. According to four criteria of the objective function average, standard deviation, number of population, and maximum number of iterations, the performance of the algorithms in the present study is compared to the performance of some algorithms in other literatures using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results of TOPSIS indicated that HWGA ranked first and WOA ranked last in both benchmark systems.
Majid Mohammadi; Saeed Farzin; Sayed-Farhad Mousavi; Hojat Karami. Investigation of a New Hybrid Optimization Algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems. Water Resources Management 2019, 33, 4767 -4782.
AMA StyleMajid Mohammadi, Saeed Farzin, Sayed-Farhad Mousavi, Hojat Karami. Investigation of a New Hybrid Optimization Algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems. Water Resources Management. 2019; 33 (14):4767-4782.
Chicago/Turabian StyleMajid Mohammadi; Saeed Farzin; Sayed-Farhad Mousavi; Hojat Karami. 2019. "Investigation of a New Hybrid Optimization Algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems." Water Resources Management 33, no. 14: 4767-4782.
Considering the great importance of flood prediction, flood routing based on Shark Algorithm (SA) and Four-Parameter Nonlinear Muskingum (FPNM) has been proposed in the present study. In fact, the Muskingum model is considered as one the most efficient method for predicting flood. However, to successfully implement Muskingum Model, there is a need to compute various parameters of this model utilizing a lot of data that characterized the physical features of the catchment. Therefore, there is a need to integrate the Muskingum model with an optimization method. Nevertheless, there are several drawbacks including trapping in local optima, overhead response and convergence time-consuming have been experienced using the existing optimization methods. Therefore, in this study, a proposal for utilizing an integrated evolutionary computing method namely; SA with FPNM has been introduced to overcome such drawbacks. Three case studies based on the definition of objective functions and different error indices were used to evaluate the algorithm. The results showed that SA significantly reduced the sum of the total square deviations (SSQs) and the sum of absolute deviation (SAD) between the predicted and observed discharges compared to other evolutionary algorithms. Moreover, the proposed model achieved high ability to accurately determine the peak value and peak time of the discharge. In addition, the calculated hydrodynamic shape has a high correlation with observed hydrographs.
Nazanin Farahani; Hojat Karami; Saeed Farzin; Mohammad Ehteram; Ozgur Kisi; Ahmad El Shafie. A New Method for Flood Routing Utilizing Four-Parameter Nonlinear Muskingum and Shark Algorithm. Water Resources Management 2019, 33, 4879 -4893.
AMA StyleNazanin Farahani, Hojat Karami, Saeed Farzin, Mohammad Ehteram, Ozgur Kisi, Ahmad El Shafie. A New Method for Flood Routing Utilizing Four-Parameter Nonlinear Muskingum and Shark Algorithm. Water Resources Management. 2019; 33 (14):4879-4893.
Chicago/Turabian StyleNazanin Farahani; Hojat Karami; Saeed Farzin; Mohammad Ehteram; Ozgur Kisi; Ahmad El Shafie. 2019. "A New Method for Flood Routing Utilizing Four-Parameter Nonlinear Muskingum and Shark Algorithm." Water Resources Management 33, no. 14: 4879-4893.
Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall–runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A1B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam.
Mohammad Ehteram; El- Shafie; Lai Sai Hin; Faridah Othman; Suhana Koting; Hojat Karami; Sayed-Farhad Mousavi; Saeed Farzin; Ali Najah Ahmed; Mohd Hafiz Bin Zawawi; Shabbir Hossain; Nuruol Syuhadaa Mohd; Haitham Abdulmohsin Afan; Hin; Bin Zawawi; Mohd; Afan; Amr H. El-Shafie; Ahmed El-Shafie. Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model. Applied Sciences 2019, 9, 3960 .
AMA StyleMohammad Ehteram, El- Shafie, Lai Sai Hin, Faridah Othman, Suhana Koting, Hojat Karami, Sayed-Farhad Mousavi, Saeed Farzin, Ali Najah Ahmed, Mohd Hafiz Bin Zawawi, Shabbir Hossain, Nuruol Syuhadaa Mohd, Haitham Abdulmohsin Afan, Hin, Bin Zawawi, Mohd, Afan, Amr H. El-Shafie, Ahmed El-Shafie. Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model. Applied Sciences. 2019; 9 (19):3960.
Chicago/Turabian StyleMohammad Ehteram; El- Shafie; Lai Sai Hin; Faridah Othman; Suhana Koting; Hojat Karami; Sayed-Farhad Mousavi; Saeed Farzin; Ali Najah Ahmed; Mohd Hafiz Bin Zawawi; Shabbir Hossain; Nuruol Syuhadaa Mohd; Haitham Abdulmohsin Afan; Hin; Bin Zawawi; Mohd; Afan; Amr H. El-Shafie; Ahmed El-Shafie. 2019. "Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model." Applied Sciences 9, no. 19: 3960.
Application of a novel method of adaptive neuro‐fuzzy inference system (ANFIS) for the prediction of air temperature is investigated. The paper discusses the improvement of ANFIS when used with Genetic Algorithm (GA), Particle Swarm optimization (PSO), Ant Colony Optimization for continuous domains (ACOR), and Differential evolution (DE). For this purpose, three input of multiple variables are selected in order to predict monthly minimum, average and maximum air temperatures for 34 meteorological stations in Iran. Determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and Nash‐Sutcliffe efficiency (NSE) are used as evaluation criteria. Comparison of suggested fuzzy models indicates that the ANFIS with GA shows the best performance in prediction of the maximum temperatures. It decreases the RMSE of classic ANFIS model in the validation stage from 1.22 to 1.12°C for Mashhad, 1.26‐1.01°C for Zahedan, 1.20‐0.98°C for Ahvaz, 1.76‐1.24°C for Rasht, and 1.21‐0.95°C for Tabriz. This article is protected by copyright. All rights reserved.
Armin Azad; Hamed Kashi; Saeed Farzin; Vijay P. Singh; Ozgur Kisi; Hojat Karami; Hadi Sanikhani. Novel approaches for air temperature prediction: A comparison of four hybrid evolutionary fuzzy models. Meteorological Applications 2019, 27, 1 .
AMA StyleArmin Azad, Hamed Kashi, Saeed Farzin, Vijay P. Singh, Ozgur Kisi, Hojat Karami, Hadi Sanikhani. Novel approaches for air temperature prediction: A comparison of four hybrid evolutionary fuzzy models. Meteorological Applications. 2019; 27 (1):1.
Chicago/Turabian StyleArmin Azad; Hamed Kashi; Saeed Farzin; Vijay P. Singh; Ozgur Kisi; Hojat Karami; Hadi Sanikhani. 2019. "Novel approaches for air temperature prediction: A comparison of four hybrid evolutionary fuzzy models." Meteorological Applications 27, no. 1: 1.