javad parsa

Effective approaches and policies including identifying priorities and optimal water allocation techniques, especially in basins with different users are considered essential for sustainable development in each region. With 1100 m3 of renewable water per person per year, Iran is considered to be the most critical region in the world in terms of water resources. Unfortunately, most plans in the water sector of such countries are based on local economic growth, and no attention is paid to the amount of available water resources. Considering the issue of a water crisis and the droughts of the last few years, the issue of water resources management has gained high importance. To overcome the mentioned problems, it is inevitably essential to use newly developed water management techniques based on advanced approaches. Although optimization techniques are well-known tools in these issues, the simulation method is utilized as a helpful approach. To simulate water management in the basin, there are various available models. RIBASIM, MIKE BASIN, WEAP, and MODSIM models are famous and user-friendly ones in this collection. WEAP software is a comprehensive and advanced water resource system simulation tool widely used in watershed management and can consider physical and hydrological processes. The scenarios that can be investigated with this software include population growth, economic development, changing the policy of operating reservoirs, extracting more from underground water resources, saving water, allocating ecosystem needs, integrated use of surface and underground water, reuse of water, etc.

This study was conducted in the Nahand catchment area which is located in East Azerbaijan province. Nahand river is the main draining course of this catchment, on which a dam has been built to supply a part of Tabriz's drinking water. To control the performance indicators of the reservoir, several management and exploitation scenarios were developed and evaluated in the WEAP model. The WEAP model was presented in 1990 by the Stockholm Environment Institute (SEI). It is a comprehensive and advanced model for simulating water resource systems, which is extensively used in the management of water resources in watersheds. This model has provided a practical tool for water resource planning and policy analysis to put all the issues related to water resources and uses in a single environment. The WEAP model is capable of simulating issues related to consumption such as water consumption patterns, water reuse strategies, costs, and water allocation patterns, as well as issues related to resources such as river flow, groundwater resources, reservoirs, and water transmission lines. The inputs of the WEAP model include data on the population of Tabriz City, per capita consumption of drinking water per person, the amount of water wastage in the distribution network, the inlet discharge of the Nahand reservoir, the information of the Nahand dam, the amount of cultivated area, etc., and to evaluate the model R^2, RMSE, and MAE statistical indicators were used in two periods of calibration and validation. Then, various operating conditions were investigated by compiling the Reference (continuation of the status quo), SC1 (increase of input flow by 10%), and SC2 (decrease of input flow by 10%) scenarios. Besides, Reservoir performance indicators are used to measure its performance under different operating circumstances.

The simulation results of the studied area indicated that the WEAP model with evaluation criteria including R2, RMSE, and MAE in the calibration stage was 0.89, 1.16, and 1.01 MCM, respectively, and in the validation stage were 0.88, 6.22, and 6.01 MCM, respectively. The results also showed that the amount of water demand for the near future period (2021-2040) will increase due to the increase in population, and therefore, the resources in the basin will not be able to meet all assumed needs. The findings showed that the studied system for the near future period (2021-2040) under the reference (continuation of the status quo), SC1 (increase in flow by 10 %) and SC2 (decrease in flow by 10 %) scenarios from the drinking water supply point of view, will result in a shortage of 28.1, 7.3 and 44.3%, respectively, and from the supply of agricultural needs point of view will result in 31.4, 18.3 and 44.4%, respectively. Also, by evaluating the reservoir's performance indicators, it was found that under all assumed scenarios, the system will fail under the condition of supplying 100% and 80% of the needs, whereas the reservoir will be more sustainable by applying the SC1 scenario in comparison with the other two scenarios.

To choose the best management and exploitation scenarios, due to existing circumstances and limitations such as time limitation, cost, possible risks to the environment, etc., it is not possible to apply all scenarios in the basins and, thence, it is logical to choose the most suitable scenario. Therefore, software tools can help experts to make decisions by considering all limitations. By examining the results of the reservoir performance indicators, it can be seen that the reservoir will encounter failure in supplying 100 and 80% of the needs in the future period under all scenarios and the sustainability index of the reservoir (remedial stability) in supplying 100%. The needs under the Reference, SC1, and SC2 scenarios will reach 31, 49, and 22%, respectively, and in meeting 80% of the needs, the sustainability index will be slightly higher.

Labyrinth weirs are often a desirable design option to regulate upstream water elevations and increase flow capacity and the gate structures have some advantages including passing the floating substances and the sediments in using combined weir-gate structure. But, it can be difficult to design due to the complex flow characteristics of a labyrinth weir-gate. A labyrinth weir could be described as a continuous and broken weir plan in a trapezoidal or triangular form. Thus, for a fixed width, labyrinth weirs have a longer crest distance when compared to linear one. Most weirs create a relatively static water zone in their upstream, which can be the site for sedimentation and waste materials, which is a disadvantage of these structures. Because of the sediments deposition in upstream of the weirs the flow conditions change and the accuracy of the presented relationships is reduced. Although numerous methods of design have been published for labyrinth weirs, there is insufficient design information available about the combined models of Labyrinth weir-gate.

This study was conducted to improve labyrinth weir -gate design and analyses techniques using physical-model-based data sets. The experiments were conducted in a metal and glass flume with a rectangular cross-section. The flume was 0.25 m wide, 0.5 m deep, and 10 m long. In each test the upstream subcritical depth was measured using point gauges of 0.1 mm accuracy The location for measuring the total head of the water upstream of the weir is a horizontal distance of three to four times the maximum water head on the crest of the weir. The discharge was measured with a triangle sharp weir placed at the end of the flume. The discharge-head relationship (Q-h) for triangular weir in experiments is as Q=0.6918 h2.5. The trapezoidal labyrinth weir-gate models was installed at the distance of 3 m of the beginning of flume. The base material roughness was made of natural sand with a mean diameter of 3 mm. In this research, experimental study of combined flow trapezoidal labyrinth weir-gate with one cycle has done for three sidewall angles of 15, 20 and 25 degrees, three gate openings 2, 4 and 6 cm and the weir height of 14 cm in a rectangular channel. According to the effective parameters of the combined models including sidewall angel (), gate opening (a) and the hydraulic head (Ht), the discharge coefficient has evaluated. By applying the Buckingham π theorem an equation was obtained. The discharge coefficient of trapezoidal labyrinth weir-gate can be expressed as a function of the variables of Fr,Re,We,H_t/P,L/H_t ,H_t/a,ϕ,α. In this study, the depth of water measured on the weir crest is at least 3 cm, so the effect of surface tension on the weir (We) is negligible. The effect of dynamic viscosity on the hydraulic behavior of the flow can be ignored. Therefore, the Reynolds number (Re) can be removed.

The results show that the discharge coefficient decreases with increasing the ratio of Ht/P for both smooth and rough beds and it reaches a constant discharge coefficient for Ht/P >0.6. According to the effective parameters of the combined models, the discharge coefficient has obtained averagely in the range of 0.61-0.75. The discharge coefficient of the combined flow increases with increasing angle of the weir. The increase in discharge coefficient is due to the decrease in the length of weir which decreases the flow mixing. Also for a specified angle, the discharge coefficient increases with increasing of L/ Ht then gets a constant value. The effects of artificial roughness on discharge capacity are also presented. It can be shown that the discharge coefficient increases in rough bed condition compared to the smooth bed condition. The present test data and those of Crookston (2010) were compared and it can be seen that the discharge coefficient in combined flow trapezoidal labyrinth weir-gate is more than the discharge coefficient of the trapezoidal labyrinth weir (without gate) in Crookston (2010) investigation.

The discharge coefficient of trapezoidal labyrinth weir-gate has the highest value for weirs with a sidewall angle of 25° and gate opening of 6 cm in rough bed condition about 17% more than the smooth bed. Among the different experimental models with a sidewall angle of 25, the labyrinth weir-gate in the rough bed condition has the highest discharge coefficient (approximately 0.93) compared to the smooth bed (approximately 0.61).

Estimating the water inflows to water resources systems is one of the essential measures for awareness of planning and allocating optimal water resources in different sectors of consumption. In this study, a combination of Meta Heuristic algorithms including the Water Cycle Algorithms (WCA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Neural Network Algorithm (NNA) and Grasshopper Optimization Algorithm (GOA) for Neural-Fuzzy System training and update parameters was used. Finally, the best models were developed to predict the daily inflow of Sefidrood reservoir dam. This method does not suffer from the problems of training gradient-based algorithms. Four features including dam lake area, reservoir volume and reservoir level of the dam in the 7 lags and inlet flow in the 1 lag were selected according to the AutocVarious statistical indicators were used to evaluate the performance of utilized models. In the test stage, ANFIS-WCA model presents the lowest values of SI, MAE and NRMSE equal to 0.0736, 0.05048 and 0.0736, respectively, and the maximum value of R^2equals to 0.9840, which indicates its superiority over other models. Based on GPI index, ANFIS-WCA model was selected as the best model and then ANFIS-NNA, ANFIS-GOA and ANFIS-WOA models were ranked, while the worst accuracy was obtained through the ANFIS-GOA model. The high accuracy of the ANFIS-WCA model compared to other hybrid models indicates the performance of the WCA for escaping local optimization in combination with the ANFIS model which has enabled this algorithm as a powerful tool for estimating the inflow of Sefidrood dam.

Regarding the growing awareness of the environmental issues over the past decades, the main aspect of river engineering has been the environmental improvement of the rivers. River restoration refers to the environmental and ecological aspects of river engineering. New eco-friendly river restoration techniques such as vanes, deflectors, W-weir, U-weir, cross vane and J-hook vanes are generally designed and implemented in accordance with different hydraulic conditions to control the river bed, stabilize the banks, improve the river ecosystem conditions and to overcome the negative effects of the conventional structures on river natural functioning. These structures performance has been evaluated in many studies. Bank-attached vanes are one of the eco-friendly structures. In the present paper, the effect of the different geometry and permeability rates of these structures on turbulent flow pattern has been investigated.

Turbulent flows are simulated by solving the Navier-Stokes equations. FLUENT software and large eddy simulation (LES) mathematical model were used to solve the Navier-Stokes equations and simulate the turbulent flow field around bank-attached vanes in a straight channel. Smagorinsky-Lilly subgrid-scale model was used to model unclosed residual stress tensor (τ_ij) in the governing equations. It must be noted that subgrid-scale turbulence models in FLUENT employ the Boussinesq hypothesis to calculate subgrid-scale stresses (τ_kk). To simulate the flow field affected by different bank-attached vanes, boundary conditions, flow depth and approach flow velocity were determined. Streamwise measured and simulated free surface profiles around 30% permeable rectangular vane at different Y/L positions were compared to evaluate the numerical model accuracy. Numerical and experimental results agreed well since relative average error values were 4-5% in all cases. Furthermore, a mesh composed of approximately 150000 elements was considered as an optimum mesh for all created models to resolve flow characteristics. However, due to the different permeability rates, generating the optimum mesh for all cases it is not possible.

Velocity magnitude results around bank-attached vanes revealed the presence of two flow zones which are the main flow field, upstream and downstream separation region formed due to the local effects of the bank-attached vanes and channel constriction. These zones are separated by a fully turbulent and dynamic flow, named the detached shear layer. In general high velocity zones near the vanes tip region and channel bed effect the local scour hole characteristics, thus tip velocity and maximum velocity variations are investigated at the near bed horizontal plane. Moreover, parameters such as tip velocity, maximum velocity and flow separation angle are affected by the interaction of different eddies, varying geometry and permeability conditions. Results showed that tip velocity and maximum velocity ratios have declining trend with increasing permeability rate. Generally by increasing the permeability rate, bank-attached vanes do not have a significant effect on flow pattern. Bed shear stress is considered as one of the primary parameters that affect the main flow field and downstream separation zone, results showed that due to the local effects of the bank-attached vanes, channel constriction and interaction of the horseshoe vortices downstream of the simulated vanes, maximum bed shear stress values mainly occurred at X⁄L= 2.4 section. Furthermore, by increasing the permeability rate, maximum bed shear stress values shifted towards the channel centerline. In general, due to the triangular vanes cross-sectional opening (geometry), these structures effect on flow characteristics is smaller in comparison to the rectangular vanes.

Global warming is one of the challenges that has attracted more and more public opinion in recent years, and if wrong behavior continues, including excessive use of fossil fuels, it can become a serious threat to human life. One of the effects of global warming is climate change. The phenomenon of global warming and the resulting climate change, with changes in temperature and precipitation, have significant effects on various systems such as water resources, agriculture, and the environment. In such a way that it can be considered as a big threat to water systems all over the world. These threats are different for different regions of the world. The industrialization of societies and increasing greenhouse gases have caused climate change and seriously threaten human life. Change in rainfall is one of the important effects of climate change. Changes in precipitation have affected surface runoff and underground water sources, and in such conditions, water resources management becomes more difficult and complicated. The most reliable tool for investigating the effects of climate change on different systems is the use of climate variables simulated by coupled atmosphere-ocean general circulation models. These models can simulate climate parameters (temperature, precipitation, etc.) for future periods. But the main weakness of these models is their low spatial resolution and the simplifications they consider for climate processes. Microscale exponentials are used to cover the weakness of spatial resolution. The aim of this research is to evaluate the climate change on temperature and precipitation and its effect on the runoff entering the Nahand dam reservoir.

In this research, the daily temperature and precipitation data of Nahand basin during the period (1360-1384) were used as the base period to evaluate climate changes using the CanESM2 climate model and RCPs emission scenarios in future periods. And SDSM statistical model was used for the microscopic scale.By applying the output of the CanESM2 general circulation model, using the SDSM model under RCP 2.6, RCP 4.5 and RCP 8.5 emission scenarios, and with the help of the IHACRES conceptual integrated model, the impact of climate change on the runoff entering Nahand Dam was evaluated.Nahand Dam is one of the drinking water sources of Tabriz. Therefore, determining the incoming runoff to the tank can help to manage the system optimally. Also, to simulate the runoff entering the reservoir in future periods, the conceptual integrated model of IHACRES was used to simulate rainfall-runoff. The main goal of IHACRES model is to determine the hydrological behavior of the basin using a small number of parameters.

Based on the results of the temperature assessment in both the near future (2021-2060) and the far future (2061-2100) and under all emission scenarios, it is increasing and the temperature increase in the far future is more than the near future. The lowest and highest temperature increase is respectively related to the RCP 2.6 scenario in the near future period of 0.17°C and the RCP 8.5 scenario in the far future period to the extent of 1.01°C compared to the base period (1981-2005). By examining the trend of changes in the average precipitation in the coming periods, it can be seen that the precipitation, contrary to the temperature, is decreasing in all scenarios, so that the lowest and the highest decrease, respectively, related to the RCP 2.6 scenario in the future period is close to 7.23 mm and The RCP 4.5 scenario will be 25.77 mm in the far future period compared to the base period. Runoff will decrease in future periods under all scenarios. The lowest and highest decrease in the order of the near future (2021-2060) under the RCP 2.6 scenario is 0.08 m3s-1 (8.51%) and the far future (2061-2100) under the RCP 4.5 scenario is 0.08 m3s-1 It is 0.18 (19.15%).

In this study, the effects of climate change on the runoff entering the Nahand dam were investigated using general atmospheric circulation models (GCM) and the fifth report (AR5) of the International Panel on Climate Change (IPCC) with the CanESM2 climate model under RCPs emission scenarios. The results of the climate change study on the entrance to Nahand Dam show that the runoff will decrease under the influence of this phenomenon, so that under the RCP 4.5 scenario, the runoff will decrease by 19.15% in the far future period.

Pore water pressure, stress and settlement are the most important geotechnical parameters that must be constantly monitored during the construction of earth dams. Since measuring dam settlement directly at the time of dam construction requires cost and time, the development of artificial intelligence methods can be very effective. Most studies have been done in the field of modeling earth dams during construction with a numerical model; therefore the need for artificial intelligence modeling in this field seems to be necessary. Artificial intelligence models, including neural networks, are used for study and modeling many engineering sciences. Also, with the development of meta-heuristic algorithms, their combination with neural networks has become very wide-spreading due to more accurate results. So, The purpose of this study is to determine the most effective features in modeling settlement in the body of earthen dams at the time of construction as a case study (Kaboud-val dam) using the hybrid algorithm Dragonfly - artificial neural network in different points of the body of earthen dam at the time of construction. Therefore, in this research, new inputs in artificial intelligence modeling have been proposed for this purpose and their importance in different levels of installation has been investigated.

Kaboud-val Dam is located in Golestan province of Gorgan and around the city of Aliabad. This dam is homogeneous and has a filter and inclined drainage. In order to obtain the deformations of the body and the foundation of Kaboud-val dam, settlement plates have been installed in different sections of the body and its foundation during the construction. In this study, instrumental data related to the section of 19 Kaboud-val dam were used. Also, out of 17 pages, 4 pages named M1, M5, M9 and M13 (installed in the body and Kabudwal dam at levels 140, 152, 164 and 180 meter, respectively) have been used for modeling. By analyzing the data of section 19 pages, fill level (F), reservoir level (RL), dam construction time (T), fill rate (FR) and impounding rate (RV) for inlet and landing (P) on terms of (kp), was selected as the output of the hybrid model in the feature selection method. In this study, in order to select the best combination of input features in the artificial neural network, the dragonfly algorithm was used. Feature selection is a method of selecting a subset of related attributes (the best combination of them) that is relevant to a particular goal. The most important principle is to choose stable features and remove extra data. The combination of dragonfly algorithm with artificial neural network as DA-ANN is shown, Therefore, the dragonfly algorithm (DA) models a variety of different combinations of features with an artificial neural network and selects the best least error combination (RMSE) as the optimal artificial neural network model.

By performing a hybrid algorithm, sensitivity analysis and feature selection method, combining the four features on pages M1, M5,M9 and M13 with error values (RMSE) of 0.0023(kPa), 0.0024(kPa), 0.0026(kPa), respectively, and combining the three features on the M13 page with the value Error (RMSE) equal to 0.0035(kPa) was the best input combination. The three features of construction time, fill level and reservoir level as common features in all plates are the most effective features in modeling the settlement on selected plates. On plates mounted at higher levels, the modeling error increases, because during the test period and for plate M13 (with the highest mounting level), according to the statistical indices R^2, SI, NSE and NRMSE are equal to the values of 0.9998, 0.0062, 0.9998 and 0.0062 respectively, have poorer performance than other pages. The effect of reservoir level feature on the plates installed at higher levels due to the high sensitivity coefficient is more than other points and the fill level feature has the least effect on subsidence modeling.

The results are very important considering the cost of installing the measuring equipment and the significance of estimating the actual values in the future. The present study shows that the DA-ANN hybrid model is an important tool in predicting and selecting the best input combination for the intelligent model of the target variable of the settlement at the time of construction of earth dams. However, assessment of this model using the input data studied in different dams is necessary to ensure the application of these models in different conditions.

Accurate prediction of pore water pressure in the body of earth dams during construction is one of the most important factors in managing the stability of earth dams. In this study, using three different evolutionary neural network models including multilayer perceptron neural network with genetic algorithm, particle swarm optimization and Imperialist Competitive algorithm for estimating the pore water pressure in the body of Kabudwal earth dam at the time of construction, has been studied. Five features including fill level, construction time, reservoir level, impounding rate and fill speed during the 4-year statistical period were selected as the input of the hybrid model in piezometer EP19.7. The composition of the inputs was obtained using the feature selection method and the hybrid water cycle algorithm -artificial neural network. By performing hybrid algorithm and sensitivity analysis and feature selection method, fill level, construction time, reservoir level and dewatering speed were selected as the top four inputs, because the combination of these four features with MSE value of 1.1587 had the least error. In this study, artificial neural network weights are optimized to increase efficiency using the above three meta-heuristic algorithms. In general, according to statistical indicators, the results indicate acceptable accuracy of all three hybrid models. In terms of priority, the ANN-GA hybrid model with the highest accuracy and minimum error and values of , RMSE and MAE are equal to 0.9773, 0.0457 and 0.0399, respectively, is first priority and ANN-PSO and ANN-ICA hybrid models were given the next priorities, respectively.

Labyrinth weirs are considered as an appropriate choice to correct the weirs that are having difficulty in passing the maximum possible flow. For this purpose, in the present study, a new weir was introduced called the of pseudo-Cosine labyrinth weir. First, models with different widths and heights were built in FLUENT software as a virtual laboratory. The anti-corona algorithm and adaptive neuro-fuzzy inference system (ACVO-ANFIS) were used for predicting discharge coefficient. of pseudo-Cosine labyrinth weir. Validation of the proposed method was performed using Experimental data. Then, to identify the superior model and determine the parameters affecting the discharge coefficient of pseudo-cosine labyrinth weir, the combination of different dimensionless parameters was evaluated. The performance of the proposed method was evaluated with five statistics, including determination coefficient (R2), root means squared error (RMSE), mean absolute percentage error (MAPE), nash-sutcliffe (NSE), and relative root mean square error (RRMSE). The results showed that in low hydraulic heads, the discharge coefficient has its highest value. As the radius increases, the discharge coefficient increases due to the increase in the effective length of the labyrinths. At a constant H/W, with increasing weir height, the discharge coefficient increased. The results of the ACVO-ANFIS model showed that the input variables are the ratio of the radius to the weir height (R/W), the ratio of the length of the weir to weir height (L/W), and the ratio of the hydraulic head to the weir height (H/W), with error values R2=0.971, RMSE=00.9, MAPE=0.006, RRMSE=0.010, and NSE=0.977, the most effective parameters in determining the discharge coefficient of pseudo-cosine labyrinth weirs.

The stress created in the soil significantly affects its engineering behavior. Changing its value during the construction of earthen dams causes volume change and shear strength, causing rupture, soil compaction and settlement in earthen dams. So measuring soil stresses of dams is essential that it is done by instrumentation installed. Artificial intelligence models such as artificial neural networks for modeling many engineering applications. Also by extending the meta-heuristic algorithms, combined with neural networks have become very popular due to more accurate results.

In this study, the cross-section 19 (cross-section of the middle part of the body and dam foundation) for the modeling of soil stress were used during the construction of the dam Kaboud-val. Also Kaboud-val dam instrumentation data (derived from Golestan Regional Water Co.) was used at the time of construction during the period of 4 years. Type and number of input data is the most important thing in modeling artificial intelligence. By examining data TPC19.1 cells in section 19 in Kaboud-val dam, embankment alignment (F), the water level of the reservoir (R), the construction of the dam (T), speed filling and dewatering speed was selected for input. The soil stress (P) in the body of the dam during construction, intelligent model was selected for output. This process is most effective in a subset of features from the set of input features according to the least error, selected and additional features will be removed. In this research, a meta-algorithm (artificial neural network (NNA) algorithm) is combined with an artificial neural network (ANN) that has the ability to predict complex and nonlinear relationships and extracts effective features for modeling soil stress with appropriate accuracy. In this study, the most effective features in soil stress modeling were determined in a case study (Kaboud-val Dam) using the NNA-ANN hybrid algorithm and a comparison was made between the results of the hybrid model and the numerical model. Five features include reservoir level, fill level, dam construction time, impounding velocity and fill velocity was selected for the input.

Using hybrid algorithm and feature selection method, a combination of three features, including reservoir level, fill level, dam construction time (with RMSE equal to 0.5024) were the most effective features in modeling soil stress in the selected cell. The results showed that the hybrid model in Kaboud-val Dam (with values of R^2, RMSE, MAE and NS equal to 0.9943, 2.5653, 1.9973 and 0.9999, respectively) has better performance in modeling soil stress than the numerical model. (With values of R^2, RMSE, MAE and NS are equal to 0.9625, 26.2567, 16.6725 and 0.9772, respectively). The results showed that the reduction in the input features to reduce the time and cost reduction is more economical and more effective. Because with the increase in the number of features in the hybrid model, the increase in modeling accuracy did not occur. Sensitivity analysis showed that the dam construction time and fill level, of the highest sensitivity factor, the most important feature of the model is the total stress in cells. Modeling with the mentioned features, in another dam with a new construction site and new geotechnical specifications (Masjed Soleiman Dam) showed that the use of artificial intelligence model according to statistical indicators has more accurate answers than the numerical model.

The results showed that the use of artificial intelligence methods in the design and initial estimates of soil stress parameters in earthen dams instead of using numerical methods has high reliability and accuracy. The combination of input data in the hybrid model under study is suitable for Kaboud-val dam and Masjed Soleiman dam and the appropriate combination should be used for each construction site. By completing the number of data in different sections of the dam and the number of construction sites in areas with similar climate and geotechnical conditions, a design software can be obtained to predict the amount of soil stress during construction in the body and foundations of earth dams.

According to design criteria, site specifications, and required height, different dimensional options are considered for geometric parameters, while design gravity dams. Among options, an option that provides sustainability and low costs is considered as an executive option. In this study, the Koyna concrete gravity dam section in India and the Kalat Zavin concrete gravity dam in Khorasan Razavi province were optimized using the election meta-heuristic algorithm (EA) concerning the stability conditions. In this model, geometric parameters were considered as decision variables and dam weight as an objective function. The model obtains geometric parameters in such a way that the dam weight is minimized. To evaluate the proposed method, the Koyna dam and Kalat Zavin dam data set were used and the results were compared with Genetic (GA), Honey Bee Mating Optimization (HBMO), and Imperialist Competitive (ICA) algorithms. The results of the EA method indicate a 9.87% and 10.40% decrease in the volume of concrete consumption and a reduction in area for Koyna and Kalat Zavin dams. Based on the comparisons, the EA algorithm showed better performance in optimizing the dam section than the genetic algorithm (GA), honey bee mating algorithm (HBMO), and imperialist competitive algorithm (ICA) optimization models. optimization models. Also, the improvement rate of the EA algorithm in comparison with ICA, HBMO, and GA algorithms is equal to 8%, 6%, and 11%, respectively.

So far, several researchers have conducted many studies on the effective parameters in the design of river breakwaters, which are mostly laboratory-based and are used for limited conditions. Therefore, the aim of the present studywas to optimal design of structure and to present analytical results of Zanjanrood river breakwaters (in terms of length and distance between two consecutive breakwaters) using two optimization meta-heuristic algorithms including the Gray Wolf Algorithm (GWO) and the Election Algorithm (EA). The results were compared with artificial neural network (ANN) method. The data used were randomly divided into two parts: 75% for calibration and 25% for test. The performance of the proposed methods was evaluated using the statistical indicators of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). The optimal length of the breakwaters according to the results of GWO and EA algorithms was 19.26 and 18.12 m, respectively. Moreover, the optimal distance between two consecutive breakwaters in the optimal state was calculated to be 52.56 m. On average, according to the results of the optimization, an increase of 28.4 and 35% in length and distance between two consecutive watersheds in Zanjanrood River should be done to be within the recommended design criteria.  In comparison with two methods of EA algorithm and artificial neural network (ANN), based on statistical indicators, the results of GWO algorithm with values ​​of R2 = 0.96, RMSE 0.022 and MAE = 0.016 has a higher efficiency.

The general purpose of this paper is to select effective features and model soil stress in earth dams at the time of construction with the neural network using an optimization algorithm and then compare the results of the artificial neural network model with usual ANFIS and GEP methods. Five features including fill level, dam construction time, reservoir level (dewatering), dewatering speed and embankment speed were selected as hybrid model inputs. By performing hybrid algorithm and sensitivity analysis and feature selection method, fill level and dam construction time, the most effective features were in modeling the total stress in selected cells, because the dual composition including fill level and construction time in TPC25.1, TPC25.3 and TPC25.4 cells, The error values (MSE) of 1.523, 2.747 and 0.750 were the most effective features in these cells, respectively. In TPC25.2 cell, the selection of three features including fill level, construction time and dewatering level according to the error (MSE) value of 5.245, has the greatest effect in modeling the total soil stress in this cell. Comparison between ANN model with ANFIS and GEP showed that although the difference in the accuracy of the models is very small, it can be said that all three models had acceptable answers. The results also show that the higher the dispersion of the model input data, the more the ANFIS model has the ability to simulate than the two models ANN and GEP,

One of the basic measures in managing the stability of earth dams is to accurately estimate the amount of pore water pressure in the body of the dam during and after its construction. In this study, three different models of artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and gene expression programming (GEP) to estimate the pore water pressure in the body of Kabudwal earthen dams at the time of construction have been studied and compared. Five features including fill level, construction time, reservoir level, impounding rate and fill speed have been used during the 4-year statistical period as input of models in 4 piezometers installed in the dam body. The first three features were the most effective inputs according to the cross-correlation function. In this study, the results obtained from artificial neural network (ANN) in two piezometers according to statistical indicators, provided more accurate answers than gene expression programming (GEP) and ANFIS, but in the other two piezometers it was the opposite. Also, ANFIS and GEP models provided more accurate answers in piezometers that had higher data scatter than ANN model. Finally, based on the GEP model, mathematical relationships between input features and output variables were extracted.

Urmia Lake is the largest inland water body in Iran and the largest hyper-saline lake in the Middle East. In recent years, climate change, rainfall scarcity, increased cultivated areas in the related watershed, construction of dams and various other factors have all brought the lake's environment, hydraulic and water quality to critical condition (Zoljoodi and Didevarasl, 2014; Tourian et al., 2015; Fathian et al., 2016; Danesh-Yazdi and Ataie-Ashtiani, 2019). Moreover, the construction of the causeway has influenced the water flow in the lake, as a barrier to natural communication between the northern and southern parts. Changes in water flow conditions may affect water quality in two parts. Therefore, modeling of the lake water flow condition and its water level are crucial for identifying the problems. In the present study, the flow pattern and salinity distribution are simulated using a computational fluid dynamics model to investigate the hydrodynamic conditions and the impacts of causeway construction on Urmia Lake. Finally, to improve water exchange between the north and south parts, additional openings are proposed along the causeway. Then, the environmental impacts are evaluated for the preferred hydrodynamic modeling scenarios..

In this study, the ability of WCA-ANN hybrid algorithm to model the pore water pressure coefficient in the body of Kabudwal dam at the time of construction was investigated and the effective features were identified. Therefore, five features including fill level, time, reservoir level, dewatering rate and fill speed during the 4-year statistical period were selected as the input of the model. By running the hybrid algorithm and feature selection method, the two features of fill level and time at points RU19.1  and   RU19.2 have the greatest impact on modeling the pore water pressure coefficient. In addition to the above two features, in the points of the middle axis , the features of fill speed and reservoir level with error value (MSE) equal to 0.00006 and in points close to the dam reservoir, dewatering level and dewatering rate with error value equal to 0.00004 are effective in modeling the pore water pressure coefficient. The results showed that at points close to the dam axis, the fill level and at points farther from the middle axis construction time (with high sensitivity coefficient) was recognized as the most important features in modeling the pore water pressure coefficient with artificial intelligence models.

Necessity to a complete and accurate analysis of the crack behavior in concrete dams using new methods is felt due to the sensitivity of the cracking problem in these dams. Meanwhile, meta-heuristic algorithms have a very good performance and accuracy in evaluating and predicting problems rather than other methods. In this study, Zayandehrood arch concrete dam has been chosen as the case study and the displacements in the cracks of this dam have been investigated by using election algorithm (EA). Water level and concrete temperature from 2000 to 2013 were considered as input parameters and also horizontal and vertical displacement of cracks were selected as output parameters. The results were compared with genetic algorithm (GA) and artificial neural networks (ANN). To evaluate the performance of the proposed method, three statistical criteria including correlation coefficient (R2), root mean square error (RMSE) and Nash-Sutcliff efficiency (NSE) were utilized. The results show that EA has a higher efficiency with R2 = 0.96, RMSE = 0.022 and NSE = 0.74, compared to GA and ANN. However, due to the lack of sufficient data, the amount of regression coefficient for spillway cracks was lower than the dam cracks. It is concluded that for evaluating the displacements of cracks in concrete dams and predicting their variations in future, meta-heuristic algorithms can be utilized as a very exact and powerful method. These methods can help dam managers and decision-makers in monitoring and vulnerability analysis of dams during their operation.

GharehGheshlagh wetland is one of the most important wetlands in Iran in terms of having a variety of plant and animal species especially birds. In recent years, agricultural development and land use change in this region has reduced water areas of this wetland. The present research was conducted due to the necessity of the assessment of the variations occurred during last years and forecasting the future status of this unique ecosystem in order to make decisions about the land use management. For this purpose, Landsat 8 satellite images were used as a reliable tool to investigate the trend of changes. In this regard, the images provided in three different years were classified to cover two periods of 12-year and 15-year using Support Vector Machine and Random Forest models. Then, considering the existence of land uses including barren landscapes, saline fields, water areas, and also vegetation and agriculture in the region of GharehGheshlagh wetland, their changes were evaluated. The results represented the immethodical development in agriculture area over a 15-year period from 2001 to 2015. The land use changes had a negative impact on the wetland water areas and it had caused remarkable reduction in the water level of the wetland, which necessitates for agricultural management and prevention of its future development in the region of the wetland can be mentioned as the most important result of the present study.

Labyrinth weirs are important hydraulic structures for water level regulation and flow control in canals, rivers, and reservoirs. Due to the uneven distribution of hydraulic head on the weir crest, the discharge coefficient changes along the labyrinth weirs are noticeable. For optimal use of this type of weir, it is necessary to estimate the discharge coefficient. In this regard, in this study, using a data set including 120 experimental data collected by Kumar et al. (2011) and numerical (simulated by FLUENT software using k-ε RNG turbulence model) to optimally estimate the discharge coefficient of triangular-duckbill labyrinth weir embedded in a rectangular channel 0.28 m wide, 12 m long and 0.41 m high was addressed using modern gray wolf meta-heuristic (GWO) and election (EA) algorithms. To investigate the effect of discharge coefficient, angles of 30, 60, 90, 120, 150 and 180 degrees with weir height of 10 cm were selected and the flow conditions in all cases were considered as subcritical, turbulent and falling flow. The objective function is the sum of the squares of the difference between the computational flow and the observations defined as the minimum. Comparison of the results of GWO and EA algorithms and FLUENT software with values of R2=0.96 and NRMSE=0.052 in comparison with the observed values, shows a good agreement between the observed and computational values.

During recent decades, the excessive use of water has led to the scarcity of the available surface and groundwater resources. Quantitative and qualitative surveys of groundwater resources indicate that accurate and efficient optimization methods can help to overcome the numerous challenges in assessment of groundwater quality. For this purpose, three optimization meta-heuristic algorithms, including imperialist competitive (ICA), election (EA), and grey wolf (GWO), as well as the support vector regression method (SVR), were used to simulate the groundwater quality of the Salmas Plain. To achieve this goal, the data of the groundwater quality for the Salmas plain were utilized in a statistical period of 10 years (2002-2011). The results were evaluated according to Wilcox, Schuler, and Piper standards. The results indicated higher accuracy of the GWO-SVR method compared to the other two methods with values of R2=0.981, RMSE=0.020 and NSE=0.975. In general, a comparison of the results obtained from the hybrid methods and different diagrams showed that the samples had low hardness and corrosion. Also, the results indicated the high capability and accuracy of the GWO-SVR method in estimating and simulating the groundwater quality.

The combined weir- gate is important hydraulic structure in management of irrigation canals. On the other hand, the use of labyrinth weirs can reduce water level fluctuations due to longer crest lengths than linear weirs for a specific discharge. Because of the sediments deposition in upstream of the weirs the flow conditions change and the accuracy of the presented relationship is reduced. Therefore, the use of combined flow trapezoidal labyrinth weir-gate can be a useful solution for passage of floating substances over the weir and sediment transport under the gate. In this research, experimental study of combined flow trapezoidal labyrinth weir-gate with one cycle has done for three sidewall angles of 15, 20 and 25 degrees, three gate openings 2, 4 and 6 cm and three weir heights of 14 , 17 and 20 cm in a rectangular channel. Based on the effective parameters of the combined models, the mean discharge coefficient has obtained in the range of 0.61-0.75. The results show that the discharge coefficient decreases with increasing the ratio of Ht/P, and it reaches a constant discharge coefficient of 0.61 for Ht/P >0.6. The discharge coefficient of the combined model increases by increasing the angle of the weir. Also the results show that the discharge coefficient of trapezoidal labyrinth weir-gate has the highest value for weirs with a sidewall angle of 25 ° and gate opening of 2 cm.

The use of labyrinth weirs is one of the most effective and economical ways to increase weir efficiency by enhancing weir crown length, so that at a specified width and hydraulic height compared to other weirs, more discharge passes. In this study, the hybrid method of the Whale optimization algorithm and the fuzzy neural- inference system to estimate the optimal discharge coefficient of labyrinth weirs with the curved plan is introduced. In the proposed method, the efficiency and convergence of the Whale algorithm have been measured. The experimental data were used to validate the proposed method. The performance of the proposed method was evaluated with four statistics, including the correlation coefficient (R2), the root means square error (RMSE), the mean absolute error (MAE) and the efficiency criterion (NSE). The results indicate a good match between the observed and estimated values. The results of the WOA-ANFIS method with (H /P) and (θ), showed that the discharge coefficient of labyrinth weirs with the curved plan (Cd=0.786) in compared to labyrinth weirs with a triangular plan (Cd=0.682), it increases by 10%. Also, statistical analyzes performed on the obtained results showed that the WOA-ANFIS method with high values of RMSE = 0.021, R2 = 0.981, MAE = 0.010 and NSE = 0.976 has high efficiency in estimating the discharge coefficient of this type of weirs.

Due to the flow regime and consequently the sediment regime are not constantly in the watersheds, the prediction of sediment discharge is a great help in estimating and managing the sediment input to hydraulic structures. Measurement of sediment in the usual way is not justified in nowadays and may also lead to human error. Therefore, in this study, three meta-heuristic optimization algorithms, including imperialist competitive algorithm (ICA), grey wolf optimizer algorithm (GWO) and election algorithm (EA), were used to predict the suspended sediment load of the Zarrineh river. In order to calculate the sediment discharge by the models, firstly, the necessary statistics and data were collected from the studied station in the period 1993-2015. After processing the data, 210 corresponding discharge and sediment data were selected. The corresponding discharge-sediment data from the study station were randomly separated into two parts, 70% for training and 30% for testing. In order to evaluate the performance of the algorithms, four statistics consist of R2, RMSE, MAE and the NSE were used. The results showed that GWO algorithm with values of statistical criteria R2=0.96, RMSE=228.86 ton/day, NSE=0.74 and MAE=67.32 ton/day, has a very high accuracy compared to other algorithms used which this would lead to comprehensive planning for the design and construction of hydraulic structures.

Apricot fruit is a carbohydrate-rich commodity and is a good source of fibers, essential minerals, vitamins and organic acids. It is also rich in bioactive compounds i.e. polyphenols and carotenoids that have certain antioxidant character in biological system. It is consumed in fresh, dried and frozen forms or used for the preparation of jam, juices, nectars and extruded products. Therefore, apricot being an attractive nutritious fruit is appreciated by consumers and farmers all over the world and has gained great economic importance over the years. Apricot is a climacteric fruit that presents a high respiratory and metabolic rate and, among stone fruits, is the one that presents the highest ethylene emission. One of the major problems of apricots is the rapid postharvest softening, which limits its storability and marketability. These features results that, at the postharvest, have an extremely short shelf-life of only 1-2 weeks in the cold storage, and pass quickly from maturity to overripe. One of the major problems of apricots is the rapid postharvest softening, which limits its storability and marketability. For this reason, to have acceptable quality fruits, they must be stored with adequate techniques to limit the post-harvest losses. As apricot production and export increase, demands for practical methods of post-harvest quality retention are necessary to improve the post-harvest quality of apricots during postharvest life. Several pre- and postharvest technologies have been used to control postharvest losses, but the use of chemicals as fungicides is restricted in most countries and consumers demand agricultural commodities without pesticide residues. Consumers around the world demand for food of high-quality, without chemical preservatives, and extended shelf life. Therefore, an increased effort has been made to develop new natural preservatives and antimicrobials. Many storage techniques have been developed to extend the marketing distances and holding periods for commodities after harvest. Among these technologies, edible coatings are traditionally used to improve food appearance and preservation. Edible coatings are thin layers of edible material applied to the product surface in addition to or as a replacement for natural protective waxy coatings and provide a barrier to moisture, oxygen and solute movement. They are applied directly on the food surface by dipping, spraying or brushing. Edible coatings are used to create a modified atmosphere and to reduce weight loss during transport and storage. In fact, the barrier characteristics of gas exchange for films and coatings are the subjects of much recent interest. Recently, researchers have developed a gel based on Aloe vera that prolongs the conservation of fresh fruits. This gel is tasteless, colorless and odorless. This natural product is a safe and environmental friendly alternative to synthetic preservatives such as sulfur dioxide. This gel operates through a combination of mechanics, forming a protective layer against the oxygen and moisture of the air and inhibiting the action of micro-organisms that cause food-borne illnesses through its various antibacterial and antifungal compounds. Aloe vera gel-based edible coatings have been shown to prevent moisture and firmness losses, control respiratory rate and delay ripening process slow oxidative browning and reduce microorganism proliferation in fruits. Present study was conducted to evaluate the increasing of apricot storage time, introducing suitable cultivars based on Aloe vera gel coating, effects of Aloe vera gel on the qualitative characteristics of fruit and obtain the optimum concentration of Aloe vera gel required to coating apricot fruits.

Therefore, a factorial experiment was carried out based on a completely randomized design with four replications. The evaluated factors including cultivars “Dorosht-e Malayer” and “Ghermez-e Shahrood”, concentrations of Aloe vera gel [control (non-gel coating fruits), 25% and 33%] and storage time [0 (Harvest), 7, 14 and 21 d)]. Moreover, total soluble solids (TSS), titratable acidity (TA), fruit firmness, pH, percentage of weight loss, antioxidant activity and vitamin C content were determined during storage time.

The results showed that treatment of apricot fruits with Aloe vera gel, especially at 33% concentration, significantly enhanced vitamin C content, titrable acidity, antioxidant capacity and improved tissue firmness, but also reduced soluble solids, weight loss and pH content. Moreover, it was found that vitamin C content, titratable acidity and fruit tissue firmness significantly decreased during storage time, while soluble solids content, weight loss and antioxidant capacity significantly increased. Among the cultivars, “Ghermez-e Shahrood” cultivar had higher soluble solids content, weight loss, pH and antioxidant capacity during storage time, whereas “Dorosht-e Malayer” cultivar had higher titrable acidity, tissue firmness and vitamin C at the end of storage time. double and triple interaction effects of evaluated factors significantly affected all characteristics, except in antioxidant activity, where just storage time had a significant effect. It was found that the highest vitamin C content in “Dorosht-e Malayer” and “Ghermez-e Shahrood” was obtained at the end of the second week in coated fruits with 33% of Aloe vera gel. TSS content in fruits of “Dorosht-e Malayer” cultivar that coated with 25% and 33% of Aloe vera gel was less than “Ghermez-e Shahrood” cultivar, while Aloe vera gel had no significant effect on TSS content in “Ghermez-e Shahrood” cultivar. Aloe vera gel had no defined pattern on TA concentration but generally, “Dorosht-e Malayer” fruits had higher TA concentration comparing “Ghermez-e Shahrood” fruits During the storage time, the fruit weight loss and firmness were higher in “Ghermez-e Shahrood” as compared with “Dorosht-e Malayer”, however, in the both evaluated cultivars, treatment with Aloe vera gel had a significant positive effect to controlling of fruit weight loss and firmness. Furthermore, coating with Aloe vera gel improved the antioxidant capacity in both evaluated cultivars.

Overall, according to the results, it can be concluded that as compared with “Ghermez-e Shahrood” cultivar, “Dorosht-e Malayer” cultivar showed better quality based on the evaluated parameters and maintained its quality for a longer time. As a conclusion, considering the positive effects of Aloe vera gel, it can be in order to increase the shelf-life and maintaining the quality of apricot fruits.

Urmia Lake is the largest inland water body in Iran and the second hyper-saline lake in the world. It has been designated as a “conserved region of biosphere” by UNESCO due to its environmental importance and unique aquatic ecosystem. The lake has faced a variety of natural and anthropogenic hazards in recent years and has encountered dramatic changes in its natural hydrodynamic condition. There are a number of reasons for these changes, where primary causes are increased water consumption, especially in the agricultural sector, development of water storage structures in the lake basin, construction of the causeway, climate change, global warming, and droughts. Due to changes in the lake's natural condition, the study of its hydrodynamic pattern is inevitable. In this regard, the current study aims to simulate changes in the water temperature of Lake Urmia in order to study its hydrodynamic. MIKE hydrodynamic models are developed by DHI Water and Environment for simulation of flows in estuaries, bays, coastal areas, lakes, and oceans. To perform the simulations in this study, MIKE3 model was used. In the modeling process, the flow simulation is carried out simultaneously with the heat transfer model, covering all the hydrodynamic conditions of the lake. In order to simulate water temperature and its effects on the lake hydrodynamics in the MIKE model, density is considered as a function of temperature, then the heat transfer equations are solved at each time step. The forces governing the hydrodynamic equations include wind, air pressure, tide, wave, and Coriolis forces. However, the forces governing heat transfer/diffusion equations are of a different nature. Air temperature, relative humidity, and clearness coefficient are important inputs for the simulation of changes in the water temperature in the lake. Due to the high accuracy of the obtained values, ECMWF model data were used in the model. The validity of the numerical model was also assessed by comparing the simulated results against satellite data. The information is provided by the Group for High Resolution Sea Surface Temperature (GHRSST). In this group, global sea surface temperature data are generated using a multi-scale two-dimensional variational (MS-2DVAR) blending algorithm. These sea surface temperature data are obtained from various satellites with multiple sensors (such as AVHRR, AATSR, SEVIRI, AMSRE, TMI, MODIS, GOES, MTSAT-1R, etc.). In this study, changes in the water temperature of Lake Urmia were simulated in order to study its hydrodynamic condition. Initially, the data used to simulate water temperature changes are presented. These data included air temperature, relative humidity, and clearness coefficient. Due to the high accuracy and generalizability of the ECMWF model output to the entire computational domain, the output of this model were used to obtain the above data. MIKE3 hydrodynamic model was used to perform the simulations. In order to investigate the effects of precipitation, evaporation, and rivers discharge on water temperature changes, two models, one with and the other without considering these factors were implemented. The water temperatures were compared in these two models. The results showed that water temperature values were approximately the same for the two cases. Also, a comparison between the water temperature output results at different depths revealed that due to the low depth of the lake, the temperature difference between the surface layer and the near-bed layer was low and reached a maximum of 0.2 °C. In addition, GHRSST satellite data was used to validate the model results. Evaluations indicated that the model results were in good agreement with the measured data, and seasonal variations in lake surface temperature were also well simulated. Moreover, the effect of causeway on the spatiotemporal distribution of lake water temperature has been investigated. For this purpose, simulation of temperature changes was considered over a one year period. The results demonstrated that the water temperature of the lake did not change significantly in both with and without causeway, and the temperature exchange between northern and southern parts of the lake occurred in both conditions. Hence, this model can be used as an efficient tool to assess the effect of causeway on the flow pattern, salinity distribution and sedimentation process in both parts of the lake

Estimation of the ecological level of water bodies is critical to protect aquatic ecosystems and has become a major issue in sustainable water resources planning. In recent decades, several methods are utilized to estimate the minimum ecological flow in rivers and the minimum ecological level in lakes and wetlands. In this research, a multi-objective programming model, which is a comprehensive ecological level or environmental flow estimator model, has been used to determine the ecological level of the GooriGol wetland. The model has two objective targets with two indices of water and ecosystem indicators. The level of water in the wetland has been selected as a water index and three species of important ducks in the wetland have been chosen as the ecosystem indices. The first objective function is to minimize the water index, while the second objective function is to maximize the ecosystem indices, so that more habitats are provided for aquatic ecosystems. Therefore, the aim of this model is to provide conditions for the largest ecological operations with the least amount of water. In order to solve the multi-objective optimization model, the sum of weighting technique is used. The computational results indicated that the minimum ecological level of the GooriGol wetland is 1912.6 meter. The field observations during the recent decade are in agreement with the obtained result of this research and indicates the decreasing the water level from 1912.6 m causes considerable declining in the ecological performance of the wetland.