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Behrouz Asakereh

Bio: Behrouz Asakereh is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Water quality. The author has an hindex of 1, co-authored 1 publications receiving 35 citations.
Topics: Water quality

Papers
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Journal ArticleDOI
TL;DR: In this paper, the chemical parameters of the Karun River (within the Ahwaz-Mollasani) have been investigated in order to determine the corrosion or sedimentation in the irrigation system under pressure.
Abstract: Water quality is as important as its quantity. Water in nature often has impurities that prevent the use of this vital substance. For this reason, different indicators are presented for the detection and elimination of impurities in the water. In this paper, the chemical parameters of the Karun River (within the Ahwaz-Mollasani) have been investigated in order to determine the corrosion or sedimentation in the irrigation system under pressure. For this purpose, two Ryznar and Langelier indicators have been used and after the necessary calculations it has been determined that the river water has a negative Langelier index (LSI 8.5) during the specified time interval, indicating high acidity during this time period, which causes deterioration and decay in marine structures (especially low pH areas).

55 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, an artificial neural network analyst model was advanced based on the information from the well-tested model HYDRUS-2D/3D, and the methodological process for defining the drainage retention capacity of surface layers under conditions of unsteady-state groundwater flow was demonstrated.
Abstract: The methodological process for defining the drainage retention capacity of surface layers under conditions of unsteady-state groundwater flow was demonstrated. An artificial neural network analyst model was advanced based on the information from the well-tested model HYDRUS-2D/3D. Artificial neural network knowledge is reported as an intermittent to physical-based modeling of subsurface water distribution from trickle emitters. Three options are prospected to create input-output functional relations from information created using a numerical model (HYDRUS-2D). Artificial neural networks are a tool for modeling of non-linear systems in various engineering fields. These networks are effective tools for modeling non-linear systems. Each artificial neural network includes an input layer and an output layer between which there are one or some hidden layers. In each layer, there are one or several processing elements or neurons. The neurons of the input layer are independent variables of the understudy issue and the neurons of the output layer are its dependent variables. An artificial neural system, through exerting weight on inputs and by using an activation function, attempts to achieve a desirable output. In this research, in order to calculate the drain spacing in an unsteady state in a region situated in the northeast of Ahwaz, Iran, with different soil properties and drain spacing, the artificial neural networks have been used. The neurons in the input layer were specific yield, hydraulic conductivity, depth of the impermeable layer, and height of the water table in the middle of the interval between the drains in two-time steps. The neurons in the output layer were drain spacing. The network designed in this research included a hidden layer with four neurons. The distance of drains computed via this method had a good agreement with real values and had a high precision in comparison with other methods. This was done for three types of linear activation functions and hyperbolic and sigmoid tangents. The mean error was 0.1455, 0.092, and 0.0491, respectively.

88 citations

Journal ArticleDOI
TL;DR: In this paper, a study was conducted to determine the stable and instable eco-hydrologic regions in the study area and the results indicated that most of the areas had not a suitable condition in terms of stability at the downstream reaches.
Abstract: Construction of dams has a significant impact on hydrological conditions of rivers. Eco-hydrology, as a sub-discipline of hydrology, focusses on ecological processes occurring within the hydrological cycle and strives to utilize them for enhancing the environmental sustainability. The aim of this study was to determine the stable and instable eco-hydrologic regions in the study area. First, the factors affecting the eco-hydrologic stability were selected according to field surveys. Afterwards, the layers related to each factor were prepared in geographic information system (GIS) and ArcGIS 10 software. These factors were also weighted using the analytic hierarchy process and pairwise comparisons. Ultimately, the final map was prepared by integrating and determining the homogenous units. The CEQUALW2 software, as a water quality and hydrodynamic model, was used to confirm the accuracy of the quality data and to perform the water quality simulation in the studied dam. According to the results, pollutant source and water quality were found to be the most important factors. The final map indicated that most of the areas had not a suitable condition in terms of stability at the downstream reaches.

76 citations

Journal ArticleDOI
TL;DR: In this article , the Valiantzas equation has been applied for calculating the water advance period in an indistinct and detailed way, which has been assumed from the consequences of the zero inertia prototypical.
Abstract: Abstract The foremost aim of the channel irrigation is a suitable choice of preparation and decision-making flexibles. These flexibles are the channel length, current degree to the channel and cutoff period. These flexibles are calculated through optimization depending on diminishing the overall irrigation cost and maximizing the application competence of irrigation. The goal meaning has been shaped depending on costs of the water, employee and head channel and channel excavation. So, in the impartial purpose, an equation should be measured for calculating the water advance period in an indistinct and detailed way. Subsequently, none of the careful approaches applied for advance channel irrigation such as zero inactivity calculate the advance time overtly; therefore, in this investigation the Valiantzas equation has been applied which has been assumed from the consequences of the zero inertia prototypical. In the impartial purpose, in addition to the preparation flexibles, soil features, channel and net irrigation condition have been included. So, the project variables and irrigation competence can be calculated for each kind of soil and exact herbal. An example of this project has been existed in this investigation.

63 citations

Journal ArticleDOI
TL;DR: In this paper, three machine learning models, viz. long short-term memory (LSTM), multi-linear regression (MLR), and artificial neural network (ANN), have been trained.
Abstract: Forecasting the irrigation groundwater parameters helps plan irrigation water and crop, and it is commonly expensive because it needs various parameters, mainly in developing nations. Therefore, the present research’s core objective is to create accurate and reliable machine learning models for irrigation parameters. To accomplish this determination, three machine learning (ML) models, viz. long short-term memory (LSTM), multi-linear regression (MLR), and artificial neural network (ANN), have been trained. It is validated with mean squared error (MSE) and correlation coefficients (r), root mean square error (RMSE), and mean absolute error (MAE). These machine learning models have been used and applied for predicating the six irrigation water quality parameters such as sodium absorption ratio (SAR), percentage of sodium (%Na), residual sodium carbonate (RSC), magnesium hazard (MH), Permeability Index (PI), and Kelly ratio (KR). Therefore, the two scenario performances of ANN, LSTM, and MLR have been developed for each model to predict irrigation water quality parameters. The first and second scenario performance was created based on all and second reduction input variables. The ANN, LSTM, and MLR models have discovered that excluding for ANN and MLR models shows high accuracy in first and second scenario models, respectively. These model’s accuracy was checked based on the mean squared error (MSE), correlation coefficients (r), and root mean square error (RMSE) for training and testing processes serially. The RSC values are highly accurate predicated values using ANN and MLR models. As a result, machine learning models may improve irrigation water quality parameters, and such types of results are essential to farmers and crop planning in various irrigation processes.

52 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the initial erosion threshold shear stress, the impact of consolidation and sediment depth were examined by cylindrical settling columns, and the results showed that the concentration of eroded sediments is a function of time for the consolidation of reservoir sediment and bed shear stresses and also observed that the duration of consolidation time is an effective factor on critical erosion shear stressed.
Abstract: The awareness of the transmission of the sticky sediments for the development and maintenance of reservoirs and water transfer network is very important. This research was carried out to recognize and understand the dynamic behavior of fine-sticky sediments to obtain the necessary information for the Karkheh dam reservoir management. Sediment samples were taken from the four different points located in the dam reservoir. Liquidity and plasticity behaviors and their indices of the samples that were combined together were determined by doing the Atterberg limits experiment. To investigate the initial erosion threshold shear stress, the impact of consolidation and sediment depth were examined by cylindrical settling columns. Using a circular flume in, Shahrekord University Lab, the concentration process, changes of eroded sediments, shear stress threshold of erosion, erosion rates, etc. in different consolidation periods (3, 14 and 30 days) were studied. The results showed that the concentration of eroded sediments is a function of time for the consolidation of reservoir sediment and bed shear stress and also observed that the duration of consolidation time is an effective factor on critical erosion shear stress. So, the threshold shear stress values for consolidation time of 3, 14 and 30 days were, 0.16, 0.22, 0.31 N/m2, respectively. The results of the erosion rate suggest an inverse relationship between this parameter and the life of the settled sediments based on the results the best flow shear stress for sediment removal by flashing from the Karkheh dam reservoir should be greater than 0.31 N/m2.

43 citations