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Ahmad Sharafati
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

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Article
Published: 21 July 2021 in Water Resources Management
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This study proposes a new stochastic approach for optimizing diversion system design and its construction schedule by considering different hydrological and hydraulic uncertainties sources. For this purpose, a multi-objective optimization-simulation model was developed to evaluate the failure of a diversion system to flood. Two objective functions, the expected flood damage (EFD) and cost-benefit (CB) index of a diversion system, are optimized in this study using a non-dominated sorting genetic algorithm II (NSGA-II). The approach is tested for four different compositions of uncertainties (Base Case, Case1, Case2, and Case3) to estimate their impacts based on distance index (D) and the boxplot. Finally, finance constraints are evaluated based on the construction period of the project. The Karun-4 dam, located in Iran, is considered as the case study. The obtained results demonstrate that the hydrological uncertainty with \({D}_{case2}^{basecase}=21.335\) and \({IQR}_{basecase}=2.1M\) has the highest effect on the Pareto optimal front and the hydraulic uncertainty of downstream cofferdam with \({D}_{case3}^{basecase}=5.789\) and \({IQR}_{case2}=1.8M\) has the lowest effect on the Pareto optimal front. The best value of the CB index is related to the base case (66.42%) using the pseudo weight factor. The study indicates that the total investment of the water diversion system is lower than the consultant's plan by 20.23%, 18.33%, 17.28%, and 18.81% when the different components of uncertainty are considered. An implementation period of 6-year and 11-year is the best option for no financial constraints and financial constraints, respectively. The stochastic simulation-optimization approach proposed in the present study provides decision-makers reliable insight into planning dam construction.

ACS Style

Ahmad Sharafati; Siyamak Doroudi; Shamsuddin Shahid; Ali Moridi. A Novel Stochastic Approach for Optimization of Diversion System Dimension by Considering Hydrological and Hydraulic Uncertainties. Water Resources Management 2021, 1 -29.

AMA Style

Ahmad Sharafati, Siyamak Doroudi, Shamsuddin Shahid, Ali Moridi. A Novel Stochastic Approach for Optimization of Diversion System Dimension by Considering Hydrological and Hydraulic Uncertainties. Water Resources Management. 2021; ():1-29.

Chicago/Turabian Style

Ahmad Sharafati; Siyamak Doroudi; Shamsuddin Shahid; Ali Moridi. 2021. "A Novel Stochastic Approach for Optimization of Diversion System Dimension by Considering Hydrological and Hydraulic Uncertainties." Water Resources Management , no. : 1-29.

Journal article
Published: 06 July 2021 in Environmental Monitoring and Assessment
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The transient storage model (TSM) is a common approach to assess solute transport and pollution modeling in rivers. Several formulas have been developed to estimate TSM parameters. This study develops a new hybrid optimization algorithm consisting of the dragonfly algorithm and simulated annealing (DA-SA) algorithms. This robust method provides accurate formulas for estimating TSM parameters (e.g., kf, T, [Formula: see text]). A dataset gathered by previous scholars from several rivers in the USA was used to assess the proposed formulas based on several error metrics ([Formula: see text] and [Formula: see text]) and visual indicators. According to the results, DA-SA-based formulas adequately estimated the [Formula: see text] ([Formula: see text], [Formula: see text]), [Formula: see text] ([Formula: see text] [Formula: see text]), and [Formula: see text] ([Formula: see text] [Formula: see text]) parameters. Moreover, the DA-SA-1 showed higher accuracy by improving the RMSE and MAE by 98% compared to the DA and DA-SA-1 as alternatives. The formulas developed in this study significantly outperformed the results of previously proposed models by enhancing the NSE up to 70%. The hybrid DA-SA algorithm method proved highly reliable models to estimate the TSM parameters in the water pollution routing problem, which is vital for reactive solute uptake in advective and transient storage zones of stream ecosystems.

ACS Style

Mohammad Ehteram; Ahmad Sharafati; Seyed Babak Haji Seyed Asadollah; Aminreza Neshat. Estimating the transient storage parameters for pollution modeling in small streams: a comparison of newly developed hybrid optimization algorithms. Environmental Monitoring and Assessment 2021, 193, 475 .

AMA Style

Mohammad Ehteram, Ahmad Sharafati, Seyed Babak Haji Seyed Asadollah, Aminreza Neshat. Estimating the transient storage parameters for pollution modeling in small streams: a comparison of newly developed hybrid optimization algorithms. Environmental Monitoring and Assessment. 2021; 193 (8):475.

Chicago/Turabian Style

Mohammad Ehteram; Ahmad Sharafati; Seyed Babak Haji Seyed Asadollah; Aminreza Neshat. 2021. "Estimating the transient storage parameters for pollution modeling in small streams: a comparison of newly developed hybrid optimization algorithms." Environmental Monitoring and Assessment 193, no. 8: 475.

Research article
Published: 28 June 2021 in Frontiers of Structural and Civil Engineering
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The scouring phenomenon is one of the major problems experienced in hydraulic engineering. In this study, an adaptive neuro-fuzzy inference system is hybridized with several evolutionary approaches, including the ant colony optimization, genetic algorithm, teaching-learning-based optimization, biogeographical-based optimization, and invasive weed optimization for estimating the long contraction scour depth. The proposed hybrid models are built using non-dimensional information collected from previous studies. The proposed hybrid intelligent models are evaluated using several statistical performance metrics and graphical presentations. Besides, the uncertainty of models, variables, and data are inspected. Based on the achieved modeling results, adaptive neuro-fuzzy inference system-biogeographic based optimization (ANFIS-BBO) provides superior prediction accuracy compared to others, with a maximum correlation coefficient (Rtest = 0.923) and minimum root mean square error value (RMSEtest = 0.0193). Thus, the proposed ANFIS-BBO is a capable cost-effective method for predicting long contraction scouring, thus, contributing to the base knowledge of hydraulic structure sustainability.

ACS Style

Ahmad Sharafati; Masoud Haghbin; MohammadAmin Torabi; Zaher Mundher Yaseen. Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related uncertainties. Frontiers of Structural and Civil Engineering 2021, 15, 665 -681.

AMA Style

Ahmad Sharafati, Masoud Haghbin, MohammadAmin Torabi, Zaher Mundher Yaseen. Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related uncertainties. Frontiers of Structural and Civil Engineering. 2021; 15 (3):665-681.

Chicago/Turabian Style

Ahmad Sharafati; Masoud Haghbin; MohammadAmin Torabi; Zaher Mundher Yaseen. 2021. "Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related uncertainties." Frontiers of Structural and Civil Engineering 15, no. 3: 665-681.

Original paper
Published: 15 June 2021 in Theoretical and Applied Climatology
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Soil moisture (SM) governs the exchange of energy and water between the atmosphere and land surface. In situ measurements of SM are uneven in Iran. This knowledge gap can be filled using satellite- and model-based products. This study assessed the performance of SM products, including Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer (AMSR2), and Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) against in situ observations considering the influence of soil texture, climate, and land cover over Lake Urmia Basin, which is the largest salt lake in Iran and the Middle East. In situ SM was measured over Lake Urmia Basin in the morning and afternoon using the time domain reflectometry (TDR) and oven drying and weighing techniques. Five statistical indicators, including correlation (R), absolute correlation (R(abs)), bias, root mean square error (RMSE), and unbiased root mean square error (ubRMSE), were applied. C-band AMSR2 products showed the best performance in grassland and croplands with the highest absolute correlation (0.63) and lowest average bias (−0.01). Among soil textures, SM products performed better in clay soils with the highest absolute correlation between C-band AMSR2 products and in situ observations (0.64) and low average bias and RMSE. Analyzing data based on climate, AMSR2 C1, and GLDAS products with the lowest average RMSE (0.08 m3m−3) and bias (0.01) and AMSR2 C2 with the absolute correlation of 0.6 showed the best performance in both temperate (Csa) and cold (Dsa) climate classes. For all classifications (land cover, soil texture, climate divisions), SMAP products reported the lowest average value of ubRMSE (0.03 m3m−3). The major contribution of the paper is finding the best SM products that can fill the gap in SM measurements data in Lake Urmia. In this analysis, the impacts of land cover, climate, and soil texture on the performance of products were considered.

ACS Style

Mohammad Saeedi; Ahmad Sharafati; Ameneh Tavakol. Evaluation of gridded soil moisture products over varied land covers, climates, and soil textures using in situ measurements: A case study of Lake Urmia Basin. Theoretical and Applied Climatology 2021, 145, 1053 -1074.

AMA Style

Mohammad Saeedi, Ahmad Sharafati, Ameneh Tavakol. Evaluation of gridded soil moisture products over varied land covers, climates, and soil textures using in situ measurements: A case study of Lake Urmia Basin. Theoretical and Applied Climatology. 2021; 145 (3-4):1053-1074.

Chicago/Turabian Style

Mohammad Saeedi; Ahmad Sharafati; Ameneh Tavakol. 2021. "Evaluation of gridded soil moisture products over varied land covers, climates, and soil textures using in situ measurements: A case study of Lake Urmia Basin." Theoretical and Applied Climatology 145, no. 3-4: 1053-1074.

Research article atmospheric and space sciences
Published: 28 May 2021 in Acta Geophysica
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In recent years, gridded precipitation products have been widely used in hydrology studies and other fields of water sciences. This study evaluated the potential of several gridded precipitation products, including GPCC, TRMM, CRU, ERA-Interim, and ERA5, in trend analysis of precipitation depth and the number of rainy days in various regions of Iran. Moreover, the observational precipitation data of the daily time series were collected from 68 Iranian synoptic stations. The Mann–Kendall test was conducted to determine gridded and observed precipitation trends in the period of 1997 to 2017. The probability of detection (POD) and false alarm ratio (FAR) indices were utilized to compare gridded and observed precipitation trends. Results showed that the best consistency (POD: 52% ~ 80%, FAR: 60% ~ 88%) was observed between the observed trends of the number of rainy days and those obtained by TRMM product over different regions of Iran. Moreover, ERA-Interim offered a better performance (POD: 50% ~ 100%, FAR: 58% ~ 72%) in the trend analysis of precipitation depth in Iran. The consistency between observational and gridded precipitation trends has never been analyzed in Iran at this level; therefore, this is considered a unique analysis. Besides, the generated maps of precipitation products' performance provide a comprehensive view of better water resources management over different regions of Iran.

ACS Style

Shahin Shobeiri; Ahmad Sharafati; Aminreza Neshat. Evaluation of different gridded precipitation products in trend analysis of precipitation features over Iran. Acta Geophysica 2021, 69, 959 -974.

AMA Style

Shahin Shobeiri, Ahmad Sharafati, Aminreza Neshat. Evaluation of different gridded precipitation products in trend analysis of precipitation features over Iran. Acta Geophysica. 2021; 69 (3):959-974.

Chicago/Turabian Style

Shahin Shobeiri; Ahmad Sharafati; Aminreza Neshat. 2021. "Evaluation of different gridded precipitation products in trend analysis of precipitation features over Iran." Acta Geophysica 69, no. 3: 959-974.

Journal article
Published: 13 May 2021 in Ain Shams Engineering Journal
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In the current research, a newly developed ensemble intelligent predictive model called Bagging Regression (BGR) is proposed to predict the compressive strength of a hollow concrete masonry prism (fp). A matrix of input combinations is constructed based on several predictive variables, including mortar compressive strength (fm), concrete block compressive strength (fb), and height to thickness ratio (h/t). Three modeling scenarios based on the different data divisions (i.e., 80–20%, 75–25%, and 70–30%) for training-testing phases are evaluated. The proposed model is validated against classical support vector regression (SVR) and decision tree regression (DTR) models using statistical indicators and graphical presentations. Results indicate the superiority of the BGR over the other models. In quantitative terms, BGR attains minimum root mean square error (RMSE = 1.51 MPa) using the data division scenario of 80–20% in the testing phase, while DTR and standalone SVR models offer RMSE = 2.55 and 2.33 MPa, respectively.

ACS Style

Ahmad Sharafati; Seyed Babak Haji Seyed Asadollah; Nadhir Al-Ansari. Application of bagging ensemble model for predicting compressive strength of hollow concrete masonry prism. Ain Shams Engineering Journal 2021, 1 .

AMA Style

Ahmad Sharafati, Seyed Babak Haji Seyed Asadollah, Nadhir Al-Ansari. Application of bagging ensemble model for predicting compressive strength of hollow concrete masonry prism. Ain Shams Engineering Journal. 2021; ():1.

Chicago/Turabian Style

Ahmad Sharafati; Seyed Babak Haji Seyed Asadollah; Nadhir Al-Ansari. 2021. "Application of bagging ensemble model for predicting compressive strength of hollow concrete masonry prism." Ain Shams Engineering Journal , no. : 1.

Original paper
Published: 10 May 2021 in Theoretical and Applied Climatology
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This study represents a new strategy for assessing how climate change has impacted urban water demand per capita in Neyshabur, Iran. Future rainfall depths and temperature variations are projected using several general circulation models (GCMs) for two representative concentration pathway (RCP) (i.e., RCP45 and RCP85) scenarios using LARS-WG software. A simulator model is developed using the genetic programming (GP) model to predict future water demand based on projected climate variables of rainfall depth and maximum temperature. The period of 1996–2016 is selected as the base period. Three future periods, namely the near-future (2021–2040), middle future (2041–2060), and far future (2061–2080), are also employed to assess climate change impact on water demand. Results indicate significant increases in annual projected rainfall depth (14~53%), maximum temperature (0.04~4.21 °C), and minimum temperature (1.01~4.71 °C). The projected monthly patterns of rainfall depth and temperature are predicted to cause a 1-month shift in the water demand peak (i.e., it will occur in April instead of May) for all future periods. Furthermore, the annual water demand per capita is projected to increase by 0.5~1.2%, 1.5~3.2%, and (2.2~7.1%), during the near-, middle-, and far-future periods, respectively. The uncertainty associated with water demand is also projected to increase over time for RCP45. The mathematical expression of urban water demand based on climatic variables is vital to managing the water resources of Neyshabur. The methodology proposed in the present study represents a robust approach to assessing how climate change might affect urban water demand in cities other than Neyshabur and provides crucial information for decision-makers.

ACS Style

Ahmad Sharafati; Seyed Babak Haji Seyed Asadollah; Armin Shahbazi. Assessing the impact of climate change on urban water demand and related uncertainties: a case study of Neyshabur, Iran. Theoretical and Applied Climatology 2021, 145, 473 -487.

AMA Style

Ahmad Sharafati, Seyed Babak Haji Seyed Asadollah, Armin Shahbazi. Assessing the impact of climate change on urban water demand and related uncertainties: a case study of Neyshabur, Iran. Theoretical and Applied Climatology. 2021; 145 (1-2):473-487.

Chicago/Turabian Style

Ahmad Sharafati; Seyed Babak Haji Seyed Asadollah; Armin Shahbazi. 2021. "Assessing the impact of climate change on urban water demand and related uncertainties: a case study of Neyshabur, Iran." Theoretical and Applied Climatology 145, no. 1-2: 473-487.

Research article
Published: 02 March 2021 in Complexity
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Predicting suspended sediment load (SSL) in water resource management requires efficient and reliable predicted models. This study considers the support vector regression (SVR) method to predict daily suspended sediment load. Since the SVR has unknown parameters, the observer-teacher-learner-based Optimization (OTLBO) method is integrated with the SVR model to provide a novel hybrid predictive model. The SVR combined with the genetic algorithm (SVR-GA) is used as an alternative model. To explore the performance and application of the proposed models, five input combinations of rainfall and discharge data of Cham Siah River catchment are provided. The predictive models are assessed using various numerical and visual indicators. The results indicate that the SVR-OTLBO model offers a higher prediction performance than other models employed in the current study. Specifically, SVR-OTLBO model offers highest Pearson correlation coefficient (R = 0.9768), Willmott’s Index (WI = 0.9812), ratio of performance to IQ (RPIQ = 0.9201), and modified index of agreement (md = 0.7411) and the lowest relative root mean square error (RRMSE = 0.5371) in comparison with SVR-GA (R = 0.9704, WI = 0.9794, RPIQ = 0.8521, and md = 0.7323, 0.5617) and SVR (R = 0.9501, WI = 0.9734, RPIQ = 0.3229, md = 0.4338, and RRMSE = 1.0829) models, respectively.

ACS Style

Siyamak Doroudi; Ahmad Sharafati; Seyed Hossein Mohajeri. Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method. Complexity 2021, 2021, 1 -13.

AMA Style

Siyamak Doroudi, Ahmad Sharafati, Seyed Hossein Mohajeri. Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method. Complexity. 2021; 2021 ():1-13.

Chicago/Turabian Style

Siyamak Doroudi; Ahmad Sharafati; Seyed Hossein Mohajeri. 2021. "Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method." Complexity 2021, no. : 1-13.

Journal article
Published: 02 February 2021 in Sustainability
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An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources.

ACS Style

Ziauddin Safari; Sayed Rahimi; Kamal Ahmed; Ahmad Sharafati; Ghaith Ziarh; Shamsuddin Shahid; Tarmizi Ismail; Nadhir Al-Ansari; Eun-Sung Chung; Xiaojun Wang. Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing. Sustainability 2021, 13, 1549 .

AMA Style

Ziauddin Safari, Sayed Rahimi, Kamal Ahmed, Ahmad Sharafati, Ghaith Ziarh, Shamsuddin Shahid, Tarmizi Ismail, Nadhir Al-Ansari, Eun-Sung Chung, Xiaojun Wang. Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing. Sustainability. 2021; 13 (3):1549.

Chicago/Turabian Style

Ziauddin Safari; Sayed Rahimi; Kamal Ahmed; Ahmad Sharafati; Ghaith Ziarh; Shamsuddin Shahid; Tarmizi Ismail; Nadhir Al-Ansari; Eun-Sung Chung; Xiaojun Wang. 2021. "Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing." Sustainability 13, no. 3: 1549.

Review
Published: 05 January 2021 in Progress in Earth and Planetary Science
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The application of soft computing (SC) models for predicting environmental variables is widely gaining popularity, because of their capability to describe complex non-linear processes. The sea surface temperature (SST) is a key quantity in the analysis of sea and ocean systems, due to its relation with water quality, organisms, and hydrological events such as droughts and floods. This paper provides a comprehensive review of the SC model applications for estimating SST over the last two decades. Types of model (based on artificial neural networks, fuzzy logic, or other SC techniques), input variables, data sources, and performance indices are discussed. Existing trends of research in this field are identified, and possible directions for future investigation are suggested.

ACS Style

Masoud Haghbin; Ahmad Sharafati; Davide Motta; Nadhir Al-Ansari; Mohamadreza Hosseinian Moghadam Noghani. Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment. Progress in Earth and Planetary Science 2021, 8, 1 -19.

AMA Style

Masoud Haghbin, Ahmad Sharafati, Davide Motta, Nadhir Al-Ansari, Mohamadreza Hosseinian Moghadam Noghani. Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment. Progress in Earth and Planetary Science. 2021; 8 (1):1-19.

Chicago/Turabian Style

Masoud Haghbin; Ahmad Sharafati; Davide Motta; Nadhir Al-Ansari; Mohamadreza Hosseinian Moghadam Noghani. 2021. "Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment." Progress in Earth and Planetary Science 8, no. 1: 1-19.

Research article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E20) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model (RMSEtesting=0.002,Rtesting2=0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume.

ACS Style

Sangeeta; Seyed Babak Haji Seyed Asadollah; Ahmad Sharafati; Parveen Sihag; Nadhir Al-Ansari; Kwok-Wing Chau. Machine learning model development for predicting aeration efficiency through Parshall flume. Engineering Applications of Computational Fluid Mechanics 2021, 15, 889 -901.

AMA Style

Sangeeta, Seyed Babak Haji Seyed Asadollah, Ahmad Sharafati, Parveen Sihag, Nadhir Al-Ansari, Kwok-Wing Chau. Machine learning model development for predicting aeration efficiency through Parshall flume. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):889-901.

Chicago/Turabian Style

Sangeeta; Seyed Babak Haji Seyed Asadollah; Ahmad Sharafati; Parveen Sihag; Nadhir Al-Ansari; Kwok-Wing Chau. 2021. "Machine learning model development for predicting aeration efficiency through Parshall flume." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 889-901.

Research article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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Sediment transport in the ejector is highly stochastic and non-linear in nature, and its accurate estimation is a complex and challenging mission. This study attempts to investigate the sediment removal estimation of sediment ejector using newly developed hybrid data-intelligence models. The proposed models are based on the hybridization of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristic algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and ant colony optimization (ACO). The proposed models are constructed with various related input variables such as sediment concentration, flow depth, velocity, sediment size, Froude number, extraction ratio, number of tunnels and sub-tunnels, and flow depth at upstream of the sediment ejector. The estimation capacity of the developed hybrid models is assessed using several statistical evaluation indices. The modeling results obtained for the studied ejector sediment removal estimation demonstrated an optimistic finding. Among the developed hybrid models, ANFIS-PSO model exhibited the best predictability potential with maximum correlation coefficient values CC Train = 0.915 and CCTest = 0.916.

ACS Style

Ahmad Sharafati; Masoud Haghbin; Nand Kumar Tiwari; Suraj Kumar Bhagat; Nadhir Al-Ansari; Kwok-Wing Chau; Zaher Mundher Yaseen. Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models. Engineering Applications of Computational Fluid Mechanics 2021, 15, 627 -643.

AMA Style

Ahmad Sharafati, Masoud Haghbin, Nand Kumar Tiwari, Suraj Kumar Bhagat, Nadhir Al-Ansari, Kwok-Wing Chau, Zaher Mundher Yaseen. Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):627-643.

Chicago/Turabian Style

Ahmad Sharafati; Masoud Haghbin; Nand Kumar Tiwari; Suraj Kumar Bhagat; Nadhir Al-Ansari; Kwok-Wing Chau; Zaher Mundher Yaseen. 2021. "Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 627-643.

Original article
Published: 09 November 2020 in Journal of Flood Risk Management
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This study presents a novel stochastic simulation–optimization approach for optimum designing of flood control dam through incorporation of various sources of uncertainties. The optimization problem is formulated based on two objective functions, namely, annual cost of dam implementation and dam overtopping probability, as those are the two major concerns in designing flood control dams. The nondominated solutions are obtained through a multi‐objective particle swarm optimization (MOPSO) approach. Results indicate that stochastic sources have a significant impact on Pareto front solutions. The distance index (DI) reveals the rainfall depth (DI = 0.41) as the most significant factor affecting the Pareto front and the hydraulic parameters (DI = 0.02) as the least. The dam overtopping probability is found to have a higher sensitivity to the variability of stochastic sources compared to annual cost of dam implementation. The values of interquartile range (IQR) indicate that the dam overtopping probability is least uncertain when all stochastic sources are considered (IQR = 0.25%). The minimum annual cost of dam implementation (2.79 M$) is also achieved when all stochastic sources are considered in optimization process. The results indicate the potential of the proposed method to be used for better designing of flood control dam through incorporation of all sources of uncertainty.

ACS Style

Ahmad Sharafati; Zaher Mundher Yaseen; Shamsuddin Shahid. A novel simulation–optimization strategy for stochastic‐based designing of flood control dam: A case study of Jamishan dam. Journal of Flood Risk Management 2020, 14, 1 .

AMA Style

Ahmad Sharafati, Zaher Mundher Yaseen, Shamsuddin Shahid. A novel simulation–optimization strategy for stochastic‐based designing of flood control dam: A case study of Jamishan dam. Journal of Flood Risk Management. 2020; 14 (1):1.

Chicago/Turabian Style

Ahmad Sharafati; Zaher Mundher Yaseen; Shamsuddin Shahid. 2020. "A novel simulation–optimization strategy for stochastic‐based designing of flood control dam: A case study of Jamishan dam." Journal of Flood Risk Management 14, no. 1: 1.

Original paper
Published: 02 November 2020 in Archives of Computational Methods in Engineering
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Groundwater is one of the major resources to supply the agriculture and urban water demand. Vulnerability of groundwater resources due to chemical substances is a crucial concern for groundwater quality management. The different nitrogen compounds, especially nitrate, plays an important role in groundwater quality. In last two decades, the efficient approaches called soft computing (SC) models were used for assessing the groundwater pollution. This study aims to assess the applications of various SC models for simulating the groundwater pollution due to nitrate contamination. In this way, the past trends and current applications of those models and essential factors required for assessing the ground water quality are demonstrated. Ultimately, several research gaps and possible future research direction are proposed.

ACS Style

Masoud Haghbin; Ahmad Sharafati; Barnali Dixon; Vinod Kumar. Application of Soft Computing Models for Simulating Nitrate Contamination in Groundwater: Comprehensive Review, Assessment and Future Opportunities. Archives of Computational Methods in Engineering 2020, 28, 3569 -3591.

AMA Style

Masoud Haghbin, Ahmad Sharafati, Barnali Dixon, Vinod Kumar. Application of Soft Computing Models for Simulating Nitrate Contamination in Groundwater: Comprehensive Review, Assessment and Future Opportunities. Archives of Computational Methods in Engineering. 2020; 28 (5):3569-3591.

Chicago/Turabian Style

Masoud Haghbin; Ahmad Sharafati; Barnali Dixon; Vinod Kumar. 2020. "Application of Soft Computing Models for Simulating Nitrate Contamination in Groundwater: Comprehensive Review, Assessment and Future Opportunities." Archives of Computational Methods in Engineering 28, no. 5: 3569-3591.

Journal article
Published: 18 October 2020 in Journal of Environmental Chemical Engineering
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The Water Quality Index (WQI) is the most common indicator to characterize surface water quality. This study introduces a new ensemble machine learning model called Extra Tree Regression (ETR) for predicting monthly WQI values at the Lam Tsuen River in Hong Kong. The ETR model performance is compared with that of the classic standalone models, Support Vector Regression (SVR) and Decision Tree Regression (DTR). The monthly input water quality data including Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Electrical Conductivity (EC), Nitrate-Nitrogen ( NO3 -N), Nitrite-Nitrogen ( NO2 -N), Phosphate (PO43-), potential for Hydrogen (pH), Temperature (T) and Turbidity (TUR) are used for building the prediction models. Various input data combinations are investigated and assessed in terms of prediction performance, using numerical indices and graphical comparisons. The analysis shows that the ETR model generally produces more accurate WQI predictions for both training and testing phases. Although including all the ten input variables achieves the highest prediction performance (R2test=0.98, RMSEtest=2.99), a combination of input parameters including only BOD, Turbidity and Phosphate concentration provides the second highest prediction accuracy (R2test=0.97, RMSEtest=3.74). The uncertainty analysis relative to model structure and input parameters highlights a higher sensitivity of the prediction results to the former. In general, the ETR model represents an improvement on previous approaches for WQI prediction, in terms of prediction performance and reduction in the number of input parameters.

ACS Style

Seyed Babak Haji Seyed Asadollah; Ahmad Sharafati; Davide Motta; Zaher Mundher Yaseen. River water quality index prediction and uncertainty analysis: A comparative study of machine learning models. Journal of Environmental Chemical Engineering 2020, 9, 104599 .

AMA Style

Seyed Babak Haji Seyed Asadollah, Ahmad Sharafati, Davide Motta, Zaher Mundher Yaseen. River water quality index prediction and uncertainty analysis: A comparative study of machine learning models. Journal of Environmental Chemical Engineering. 2020; 9 (1):104599.

Chicago/Turabian Style

Seyed Babak Haji Seyed Asadollah; Ahmad Sharafati; Davide Motta; Zaher Mundher Yaseen. 2020. "River water quality index prediction and uncertainty analysis: A comparative study of machine learning models." Journal of Environmental Chemical Engineering 9, no. 1: 104599.

Original paper
Published: 14 October 2020 in Theoretical and Applied Climatology
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Satellite precipitation products are important data sources in different spatial resolutions, time scales, and spatio-temporal coverage. In this study, the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product with a high spatial resolution (0.05°) is evaluated in the period of 1987 to 2017 over different climate regions of Iran. The accuracy of the satellite product is compared with the 68 ground-based meteorological stations over different time scales (i.e., daily, monthly, and annual) and precipitation classes. Results show that the performance of CHIRPS depends on the time scale, precipitation depth, and climate type. The best performance of the product (CC = 0.80, FRMSE = 0.57, NSE = 0.63) across the country is observed in the annual time scale, while the monthly product offers the best performance in the regional scale. The product provides inadequate performance (CC = 0.34, FRMSE = 5.72, NSE = − 0.2) in daily time scale across the country and most of the climatic regions. The product is found to be most accurate in the south and southwest of the country, while the lowest performance is observed over the Caspian coast. The CHIRPS satellite provides the best performance in detection of no/tiny precipitation (POD > 0.90) and the worst performance in light and low, moderate precipitation (POD < 0.10). It is expected that the findings of the current study can be used to manage the water resources and mitigate the disaster at the national level.

ACS Style

Ali Ghozat; Ahmad Sharafati; Seyed Abbas Hosseini. Long-term spatiotemporal evaluation of CHIRPS satellite precipitation product over different climatic regions of Iran. Theoretical and Applied Climatology 2020, 143, 211 -225.

AMA Style

Ali Ghozat, Ahmad Sharafati, Seyed Abbas Hosseini. Long-term spatiotemporal evaluation of CHIRPS satellite precipitation product over different climatic regions of Iran. Theoretical and Applied Climatology. 2020; 143 (1-2):211-225.

Chicago/Turabian Style

Ali Ghozat; Ahmad Sharafati; Seyed Abbas Hosseini. 2020. "Long-term spatiotemporal evaluation of CHIRPS satellite precipitation product over different climatic regions of Iran." Theoretical and Applied Climatology 143, no. 1-2: 211-225.

Original paper
Published: 10 October 2020 in Natural Resources Research
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Distributed modeling provides for mapping of spatial and temporal patterns of highly stressed regions, and it offers local solutions to reduce stress in aquifers. In this study, the groundwater stress index (GWSI) is evaluated based on the groundwater footprint index over the Varamin aquifer in Iran. Using ArcGIS software, all necessary layers were produced and then input into the Groundwater Modeling System software to evaluate GWSI. The results show that distributed modeling offers a more accurate assessment of GWSI than water budget analysis. The minimum and maximum values of the GWSI calculated by the distributed model are 2.4 and 1.4 times, respectively, higher than those values obtained in previous studies. Besides, a significant agreement was observed between highly stressed areas and agricultural land use. Furthermore, the results obtained from comparison between stress pattern and land subsidence indicated that only 10% of the area under subsidence was caused by groundwater stress. Applying appropriate scenarios in the future can be useful to reduce water stress and its increasing trend.

ACS Style

Maryam Nayyeri; Seyed Abbas Hosseini; Saman Javadi; Ahmad Sharafati. Spatial Differentiation Characteristics of Groundwater Stress Index and its Relation to Land Use and Subsidence in the Varamin Plain, Iran. Natural Resources Research 2020, 30, 339 -357.

AMA Style

Maryam Nayyeri, Seyed Abbas Hosseini, Saman Javadi, Ahmad Sharafati. Spatial Differentiation Characteristics of Groundwater Stress Index and its Relation to Land Use and Subsidence in the Varamin Plain, Iran. Natural Resources Research. 2020; 30 (1):339-357.

Chicago/Turabian Style

Maryam Nayyeri; Seyed Abbas Hosseini; Saman Javadi; Ahmad Sharafati. 2020. "Spatial Differentiation Characteristics of Groundwater Stress Index and its Relation to Land Use and Subsidence in the Varamin Plain, Iran." Natural Resources Research 30, no. 1: 339-357.

Journal article
Published: 31 August 2020 in Journal of Hydrology
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This study presents a new strategy to predict the monthly groundwater level with short- and long-lead times over the Rafsanjan aquifer in Iran using an ensemble machine learning method called Gradient Boosting Regression (GBR). In this way, the satellite-based products, including the Tropical Rainfall Measuring Mission (TRMM) and the Gravity Recovery and Climate Experiment (GRACE) datasets, as well as the pumping rate, are used as the predictive variables in different lag times. To obtain the optimal input combinations, the Gamma Test (GT) is employed as a non-linear feature selection technique. The spatial analysis of the performance prediction is performed using several error metrics (e.g., R2, and NRMSE). Results indicate that the GBR provides a high prediction performance to predict the GWL, whereas the GRACE product is an accurate predictive variable. The correlation analysis between the predicted and observed GWL shows that the coefficient of determination values vary in the range of 0.66 to 0.94 over the different lead times. The spatial pattern of the prediction accuracy indicates that the regions with higher water depth and pumping rate offer higher performance. Moreover, the increasing trends in performance accuracy are observed from the north to south and the west to east of the Rafsanjan aquifer. In general, the proposed approach provides a reliable insight for water resources planner to make a decision based on the accurate modeling results.

ACS Style

Ahmad Sharafati; Seyed Babak Haji Seyed Asadollah; Aminreza Neshat. A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. Journal of Hydrology 2020, 591, 125468 .

AMA Style

Ahmad Sharafati, Seyed Babak Haji Seyed Asadollah, Aminreza Neshat. A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. Journal of Hydrology. 2020; 591 ():125468.

Chicago/Turabian Style

Ahmad Sharafati; Seyed Babak Haji Seyed Asadollah; Aminreza Neshat. 2020. "A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran." Journal of Hydrology 591, no. : 125468.

Research article
Published: 25 August 2020 in Advances in Civil Engineering
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An assessment of uncertainty in flood hydrograph features, e.g., peak discharge and flood volume due to variability in the rainfall-runoff model (HEC-HMS) parameters and rainfall characteristics, e.g., depth and duration, is conducted. Flood hydrographs are generated using a rain pattern generator (RPG) and HEC-HMS models through Monte Carlo simulation considering uncertainty in stochastic variables. The uncertainties in HEC-HMS parameters (e.g., loss, base flow, and unit hydrograph) are estimated using their probability distribution functions. The flood events are obtained by simulating runoff for rainfall events using the generated model parameters. The uncertainties due to rainfall and model parameters on generated flood hydrographs are evaluated using the relative coefficient of variation (RCV). The results reveal a higher RCV index for flood volume (RCV = 153) than peak discharge (RCV = 116) for a 12-hr rainfall duration. The average relative RCV (ARRCV) index computed for hydrological component (e.g., base flow, loss, or unit hydrograph) indicates the highest impact of rainfall depth on flood volume and peak. The results indicate that rainfall depth is the main source of uncertainty of flood peak and volume.

ACS Style

Ahmad Sharafati; Mohammad Reza Khazaei; Mohamed Salem Nashwan; Nadhir Al-Ansari; Zaher Mundher Yaseen; Shamsuddin Shahid. Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters. Advances in Civil Engineering 2020, 2020, 1 -9.

AMA Style

Ahmad Sharafati, Mohammad Reza Khazaei, Mohamed Salem Nashwan, Nadhir Al-Ansari, Zaher Mundher Yaseen, Shamsuddin Shahid. Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters. Advances in Civil Engineering. 2020; 2020 ():1-9.

Chicago/Turabian Style

Ahmad Sharafati; Mohammad Reza Khazaei; Mohamed Salem Nashwan; Nadhir Al-Ansari; Zaher Mundher Yaseen; Shamsuddin Shahid. 2020. "Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters." Advances in Civil Engineering 2020, no. : 1-9.

Journal article
Published: 20 August 2020 in Journal of Hydroinformatics
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Wave-induced scour depth below pipelines is a physically complex phenomenon, whose reliable prediction may be challenging for pipeline designers. This study shows the application of adaptive neuro-fuzzy inference system (ANFIS) incorporated with particle swarm optimization , ant colony (), differential evolution and genetic algorithm () and assesses the scour depth prediction performance and associated uncertainty in different scour conditions including live-bed and clear-water. To this end, the non-dimensional parameters Shields number (), Keulegan–Carpenter number () and embedded depth to diameter of pipe ratio () are considered as prediction variables. Results indicate that the model ( and ) is the most accurate predictive model in both scour conditions when all three mentioned non-dimensional input parameters are included. Besides, the model shows a better prediction performance than recently developed models. Based on the uncertainty analysis results, the prediction of scour depth is characterized by larger uncertainty in the clear-water condition, associated with both model structure and input variable combination, than in live-bed condition. Furthermore, the uncertainty in scour depth prediction for both live-bed and clear-water conditions is due more to the input variable combination than it is due to the model structure .

ACS Style

Ahmad Sharafati; Ali Tafarojnoruz; Davide Motta; Zaher Mundher Yaseen. Application of nature-inspired optimization algorithms to ANFIS model to predict wave-induced scour depth around pipelines. Journal of Hydroinformatics 2020, 22, 1425 -1451.

AMA Style

Ahmad Sharafati, Ali Tafarojnoruz, Davide Motta, Zaher Mundher Yaseen. Application of nature-inspired optimization algorithms to ANFIS model to predict wave-induced scour depth around pipelines. Journal of Hydroinformatics. 2020; 22 (6):1425-1451.

Chicago/Turabian Style

Ahmad Sharafati; Ali Tafarojnoruz; Davide Motta; Zaher Mundher Yaseen. 2020. "Application of nature-inspired optimization algorithms to ANFIS model to predict wave-induced scour depth around pipelines." Journal of Hydroinformatics 22, no. 6: 1425-1451.