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One of the management strategies of water resources systems is the combination of simulation and optimization models to achieve the optimal policies of reservoir operation in the form of specific optimization. This study utilizes an integration of the NSGA-II multi-objective algorithm and WEAP simulator model so that the first objective is to maximize the reliability of providing the needs in front of the second goal, i.e., to minimize the drawdown the water table at the end of the operation time. The dam rule curve or the amount of released volume from the reservoir is optimized to supply downstream uses in these conditions. However, in certain optimizations, the optimal solutions cannot be generalized to other possible inputs to the reservoir, and if the inflow to the reservoirs changes, the obtained optimal solutions are no longer efficient and the system must be re-optimized in the form of an optimizer algorithm. Therefore, to solve this problem, a new method is extended on the basis of the combination of the support vector machine and NSGA-II algorithm for optimal real-time operation of the system. The results demonstrate that the average error rate of optimal rules derived from support vector machines is less than 2.5% compared to the output of the NSGA-II algorithm in the verification step, which indicates the efficiency of this method in predicting the optimal pattern of the dam rule curve in real time. In this structure, based on the inflow to the reservoir, the volume of water storage in the reservoir and changes in the reservoir storage (at the beginning of the month) and the downstream demands of the current month, the optimal release amount can be achieved in real time. Therefore, the developed support vector machine has the ability to provide optimal operation policies based on new data of the inflow to the dam in a way that allows us optimally manage the system in real time.

Ideal watersheds of simple geometries, characterized by dominant processes involved in rainfall–runoff transformation, can provide model users/developers with detailed insights into hydrological components. In the present paper, some models were evaluated against available laboratory data on impervious rectangular and V-shaped (open-book) watersheds. The HEC-HMS as a numerical hydrologic model was adopted along with analytical solution of the kinematic wave approximation. The numerical methods used in HEC-HMS model involve finite difference method. Simulations were performed for various cases formed through variation of watershed slope and rainfall intensity. The outputs of the models involved runoff hydrographs, time to peak discharge and maximum discharge. For the rectangular watershed, numerical solution of KW within HEC-HMS was applied while for the V-shaped case, the numerical solution of KW in HEC-HMS and DWSM models and nonlinear reservoir scheme in SWMM model were compared. According to the results, in the rectangular watershed case, numerical model of KW within HEC-HMS showed good performance with relative accuracy of 69.2% and 96.0% in estimation of time to peak discharge and maximum discharge, respectively. In the case of V-shaped watershed, all rainfall–runoff models provided satisfactory results so that the nonlinear reservoir scheme in SWMM model outperformed the KW model. Moreover, regarding the kinematic wave solution schemes, finite difference (KW in HEC-HMS) and shock fitting (KW in DWSM) were shown to be similar in efficiency with the Nash–Sutcliffe efficiency index of 0.94.

In atmospheric sciences, forecast assessment, the process of examining the quality of forecasts, is very important. For this reason, we carried out a comprehensive evaluation of the most important global numerical weather forecast (NWP) models for the Iranian basins of Karkheh, Karun, Urmia, and Sefidrood. This study aims to evaluate the output of six different NWP models from the TIGGE database in important basins of Iran, to determine the most appropriate model for each basin. The assessment period took place between 2008 and 2018 and evaluated probabilistic and non-probabilistic methods. In the Karun Basin, the global NWP models were shown to perform better than in the other three basins. Overall, the UKMO (United Kingdom Met Office) and ECMWF (European Medium-Range Weather Forecast) performed best, while the Korean Meteorological Administration (KMA) showed the poorest performance among the models in the four basins. In the 25 mm threshold, most models underestimated precipitation events. The ECMWF model showed reliable and sharp forecasts and was better able to discriminate between low, medium, and high precipitation events compared to other models. Compared to those of other models, the UKMO model demonstrated greater brier skill scores. In general, the ECMWF, UKMO, ECCC (Environment and Climate Change Canada), and National Center for Environmental Forecast (NCEP) models performed relatively satisfactory in all four basins, although they forecasted better in basins with higher mean precipitation. These models are recommended for flood warning systems, and water resource decision making in Iran. However, all forecasts will need post-processing for operational applications.

The main objective of the present study was to investigate runoff response to climate variables as well as landuse change over the past 30 years in Tajan River Basin, using the SWAT model. After the model calibration, four different scenarios were simulated and compared. Comparison of simulated runoff results determined from different scenarios indicated that climatic variables reduced the amount of runoff while the landuse change increased this amount in most months of the year. Simulated runoff under three landuse scenarios in all months demonstrated that the runoff achieved from scenario 1 was smaller than scenarios 2 and 4. In scenario 4, the runoff amount increased by 3–21% and 0.8–13% in Kordkheil station compared to those of scenario 1 and scenario 2, respectively. Furthermore, the increase in runoff for scenario 4 is 3–19% and 2–12% in Rig Cheshmeh station relative to those of scenario 1 and scenario 2, respectively. Nonetheless, the maximum change in runoff was only 6% under climatic variables. Hence, landuse had more significant impacts on the runoff compared to climatic variables.

Combined weir-gate structure is one of the important structures which are control the water level, measure discharge and avoid sediment deposition behind the weir. In this study, first try to simulate four combined triangular weir-rectangular gates with different geometric conditions via 3D numerical software (Flow-3D) by using experimental data. Then, dimensionless analyses were done to find the non-dimension parameters affected the discharge coefficient of this structure. At the end, four different intelligent system models were used to estimate the discharge coefficient, evaluated and compared the results with each other. Results show that the Flow-3D software has a high capability to simulate the flow over the combined structure. Besides, the values of goodness of fit criteria show that the numerical solver, estimate the water head and discharge coefficient very well. Moreover, in all models, the results show that the discharge coefficient has an inverse relation with dimensionless parameters (h/b, h/d and h/y) and discharge coefficients in this study are between 0.3–0.9. On the other hand, results of accuracy analyses of four intelligent system models of MLP, RBF, GRNN and M5P show that the MLP model is the superior model in this study, and after that, the rest of models’ sort as M5P, RBF and GRNN in this study.

In this paper, a hybrid meta-heuristic artificial intelligence (AI) method, named “ANFIS–PSOGA,” is developed for modeling the discharge coefficient of circular side orifices for the first time. For this purpose, the adaptive neuro fuzzy inference system (ANFIS) network is optimized by means of two robust optimization algorithms: the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. Furthermore, the Monte Carlo simulations are utilized to enhance the performance of the AI hybrid model. Besides, the k-fold cross-validation method is employed in order to verify the numerical results. Initially, the performance of three fuzzy inference system (FIS) generations of the ANFIS model including grid partitioning, subtractive clustering and fuzzy C-means clustering are evaluated. After that, using the parameters affecting the discharge coefficient, eleven ANFIS models are defined and then the best ANFIS model and the most influencing input parameters are identified through the conduction of a sensitivity analysis. For the superior ANFIS model, mean absolute percentage error, root-mean-square error and the correlation coefficient (R) are obtained 0.021, 0.020 and 0.871, respectively. Also, the Froude number (Fr) is detected as the most effective input parameter. Moreover, the discharge coefficient of the circular side orifices and the flow field within main channels along the side orifices are simulated through the adoption of a computational fluid dynamics (CFD) model. Additionally, the flow field at three different cross sections (beginning, middle and end of orifice) along the side weir is evaluated. For instance, by advancing the flow toward the side orifice, the Froude number is reduced. Besides, at each cross section, the maximum shear stress is estimated to occur in the vicinity of the crest of the circular side orifice. According to the CFD results, the maximum lateral velocity is estimated near the circular side orifice. In addition, the results yielded by the ANFIS, ANFIS–PSO, ANFIS–PSOGA and CFD models are compared and an uncertainty analysis is carried out for the models. Based on the comparison, ANFIS–PSOGA has the best performance in simulating the discharge coefficient. Ultimately, a simple and practical computer code for the best model (ANFIS–PSOGA) is proposed which can be easily used by scholars and engineers in practical usages.

Accurate estimating of daily streamflow forecasting is one of the prominent topics in water resources activities. In this paper, an integrated method including decomposition technique based on the ensemble empirical mode decomposition (EEMD) combined with multivariate adaptive regression spline (MARS) was carried out to predict daily streamflow values. Daily streamflow value datasets collected from two stations in Iran (Gachsar and Kordkheyl) were selected. After dividing into calibration and validation datasets, each of them was decomposed by EEMD. Crow search algorithm (CSA) was used to optimize the MARS parameters (MARS-CSA). The performance of the integrated model (EEMD-MARS-CSA) was investigated by error indices (correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), as well as RMSE to standard deviation ratio (RSR)). From the results, EEMD was an important tool for increasing model accuracy and EEMD-MARS-CSA outperformed other alternative methods for daily streamflow estimation. According to one-day-ahead flow forecasting, EEMD-MARS-CSA (R = 0.94, RMSE = 5.94 m3/s (Kordkheyl) and R = 0.98, RMSE = 0.71 m3/s (Gachsar)) outperformed EEMD-MT/MARS, MT, and MARS models. Furthermore, RSR criterion of EEMD-MARS-CSA was reduced by 18%, 16%, and 17% for 3-days, 1-week, and 2-weeks-ahead streamflow forecasting compared to MARS-CSA model, respectively, for Gachsar station.

Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respectively for the optimal neural network model. Genetic programming has been more accurate than artificial neural networks and is recommended as a good way to accurately predict.

In this research, the results of experiments achieved on some mixtures of concrete comprising silica fume and paper waste ash as a substitute to Portland cement have been presented. The Portland cement was substituted with various ratios (namely 0,2.5, 5, 7.5 and 10 percent by weight of cement) of both silica fume and paper waste ash. In all mixtures of concrete, the ratio of cement to water was regarded to be persistent (with an amount of 0.4). The experiments’ findings demonstrate that the 28-days tensile, flexural and compressive strengths of concrete specimens of this study are more than the results obtained from the relationship given by both the American (ACI) and Concrete Code of Iran for normal concrete due to adding paper waste ash and Silica fume to some mixes, which indicates their positive effect in increasing mechanical properties. According to our findings, empirical relationships have been proposed for the relationship of indirect tensile strength and 28-days concrete’s compressive strength, flexural tensile strength and concrete’s 28-days compressive strength, compressive strength and specific weight, the estimation of elasticity modulus in regard to specific weight of mix designs, the growing movement of compressive strength of normal concretes by increase in the amount of cement. So that these equations can be useful to predict the behavior of the concrete containing the Silica fume and paper waste ash. The electrical resistance of mix with 10% paper waste ash was a little lower, compared to the witness concrete mix at all ages.

This study investigated the effects of climatic changes on temperature, rainfall, and runoff in the Doroudzan catchment in the northeast of Fars province, Iran. Temperature and rainfall changes in three periods including 2011–2030, 2046–2065, and 2080–2099 were downscaled and studied using 15 Coupled Model Intercomparison Project, Phase 3 (CMIP3) climatic models, under three scenarios of greenhouse gas emissions A2, B1, and A1B, from the database of the LARS-WG model. The difference in the amount of changes in temperature and rainfall in these three periods and the observational amounts under the 15 models indicated the uncertainty of the changes values. To reduce this uncertainty and limit the results to the management and planning of water resources, ensemble approach was considered. For the preparation of the ensemble approach, the parameters from the files of the 15-model scenarios were averaged so that a climatic ensemble model could be obtained for each period. Then, the runoffs of the next three periods, under the second approach and three emission scenarios, were produced using the feedforwad neural network. The results indicated an increase in the average monthly maximum temperature and the minimum temperature in all three periods under the three scenarios. The results also showed a decrease in the rainfall in the early months of the year as well as an increase in the rainfall in the spring in most scenarios. Generally, the average annual rainfall in all these three periods under the climatic ensemble model, and three emission scenarios showed a reduction in the average annual rainfall in the three periods. The maximum amount of reduction was in 2080–2099 (101 mm) under the scenario B1. Besides, a reduction occurred in the average runoff of the catchment under three ensemble models and the emission scenario in all three periods, as compared to the average of the long-term observational values in most years.

This study investigated the effectiveness of a new method of reducing scour in river bends. In this method, a perforated tube was placed along the bend on the bed and water and air were separately injected into the bend flow from both ends of the tube. The goal was to make a water and air screen to block secondary flows and prevent them from reaching the outer bank. The air jet and water jet injection modes changed the location of maximum scour depth from the outer wall to the middle of the bend, which increased the navigable width. Increasing the spacing between tube ports decreased the maximum scour depth. A port spacing of 5 cm was determined to be the optimal amount. At a bend section of 90°, the decrease in maximum scour depth was estimated to be 85% and 91% under air jet injection (qa/Q = 2.74) and water jet injection (qw/Q = 0.17), respectively. At 170°, the decrease in maximum scour depth was 79% and 86% for the air jet and water jet, respectively. The results show that the optimal effect was obtained by water jet injection.

In this study, the groundwater parameters including electrical conductivity (EC), salinity, total dissolved solids (TDS) and groundwater level (GWL) for a 15-year time series (from 2002 to 2017) of the Mighan Plain, located in Markazi Province, Iran, were simulated using a hybrid meta heuristic artificial intelligence (AI) model called “wavelet self-adaptive extreme learning machine” (WSAELM) for the first time. Initially, the detection of the most significant lags of the time-series data was conducted using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analyses. By using these lags, four WSAELM models were defined and then the superior models in simulating the TDS, EC, salinity and GWL were introduced. The values of the determination coefficient (R2), Variance Accounted for (VAF) and the Nash–Sutcliffe efficiency coefficient (NSC) for the superior model simulating salinity were computed to be 0.991, 98.124 and 0.980, respectively. Also, approximately 44% of the TDS values modeled by the best model had an error less than 10%, while roughly a third of the TDS values estimated by the model had an error more than 15%. The findings indicated that the proposed hybrid model underestimated the GWL parameter, while it performed in an overestimate way for other parameters. The results of the uncertainty analysis showed the low width of uncertainty for GWL (WUB = ± 0.798), TDS (WUB = ± 5.035), EC (WUB = ± 6.425) and salinity (WUB = ± 66.650).

Multi-reservoir systems exploitation is known as one of the most important problems in optimizing water resources. The present study aims to improve the differential evolution (DE) algorithm, promoting the contraction performance and reflection operators, by merging in the shuffled complex evolutionary (SCE). In order to use the SCE algorithm for complex water resource problems, SCE-DE algorithm was developed as well. The performed algorithm was evaluated by solving a difficult mathematical function problem using SCE and SCE-DE methods as a benchmark. The results illustrate that SCE-DE method represents a higher speed in converging to the optimum response and is closer to the absolute optimum. Moreover, the mentioned algorithms were performed for solving the optimization problems of multi-reservoir systems, and the obtained results were compared with the LP absolute response. The obtained data represent a difference of 0.009% between the response of objective function in the SCE-DE method and the LP response. However, the response of objective function of SCE differs 1.84% from that of the LP. Comparing the present observations and the other studies, the proposed algorithm has revealed a much closer optimum response to the LP response. Therefore, we recommend SCE-DE for solving complex problem of water resources.

In this paper, for the first time, to model the discharge coefficient of labyrinth weirs, the evolutionary firefly algorithm (FFA) is used for optimizing the membership functions of the adaptive neuro-fuzzy inference system (ANFIS). Also, to enhance the performance of the ANFIS and ANFIS-FFA models, the Monte Carlo simulations (MCs) are employed. Additionally, the k-fold cross-validation is utilized for training and testing the methods. Next, some input dimensionless parameters including the Froude number (Fr), the ratio of the head above the weir to the weir height (HT/P), cycle sidewall angle (α), the ratio of length of the weir crest to the channel width (Lc/W), the ratio of length of apex geometry to the width of a single cycle (A/w) and the ratio of the width of a single cycle to the weir height (w/P) are determined. After that, seven different models are developed for ANFIS and ANFIS-FFA. Then, by conducting a sensitivity analysis, the superior models (ANFIS-FFA 5 and ANFIS 5) and the most effective input parameter (Froude number) are identified. Moreover, the error distribution results exhibit that about 70% of the superior model results have errors less than 5%. Subsequently, the discharge coefficient is simulated by means of a computational fluid dynamics (CFD) model. Furthermore, a comparison of the CFD model with the ANFIS and ANFIS-FFA models reveals that the ANFIS-FFA model is significantly more accurate. Also, an uncertainty analysis is performed for the CFD, ANFIS and ANFIS-FFA models. Finally, a very simple code calculating the discharge coefficient of labyrinth wires is presented. This code can be easily employed without any knowledge on ANFIS, FFA and prior information about MATLAB.

The aim of this study is to improve the performance of the shuffled complex evolution (SCE) algorithm used in the optimization of hydropower generation in reservoirs as a complex issue in water resources. First, the SCE algorithm is merged with the differential evolution (DE) algorithm to form the SCE-DE algorithm. Then, a complex mathematical function is used as a benchmark to evaluate the performance and validate the SCE-DE algorithm and the outcomes are compared with the original SCE algorithm to show the superiority of the proposed SCE-DE algorithm. In addition, the two-reservoir system of Dez-Gotvand is considered as a real optimization problem to evaluate the performance of the SCE-DE algorithm. It is revealed that optimization by SCE-DE is much better than SCE. In conclusion, the results show that the proposed SCE-DE algorithm is a reasonable alternative to optimizing resource systems and can be used to solve complex issues of water resources.

Agricultural water resources are scarce in Iran, and agricultural water management is essential to overcome this limitation. Determination of the water productivity index (WP) using the methods of optimization of cropping patterns and optimal water allocation is the most effective tool in water management in the agricultural sector. In this study, a non-linear optimization model was used to calculate the maximum WP in the Qazvin plain irrigation network. The proposed model was applied to six different water supply scenarios including 100% and 90%, 80%, 70%, 60%, and 50% of full irrigation volume. The results showed that the scenario of 60% of full irrigation could increase the WP index by 8.5%, while with the implementation of this scenario, the net benefit from the sale of agricultural products was reduced to the amount of 247 × 109 Iranian Rials (IRR), which was 28% less than the existing conditions in the region. Therefore, the 60% water supply was selected as the economical and efficient scenario in the irrigation of Qazvin plain. It can be claimed that the most important index in agricultural water management is the WP index.

Transient flow control in Water Transmission Systems (WTS) is one of the requirements of designing these systems. Hence, among control equipment, air chambers offer the best solution to control transient flow effects, i.e. both prevents water column separation and absorbs pressure increase. It is essential to carry out an accurate and optimized design of air chambers, not only due to high costs of their manufacturing but also their important protective role. Accordingly, hydraulic design parameters comprise tank volume, diameter of nozzle and coefficients of inflow and outflow of nozzle. In this paper, it is intended to optimize these parameters in order to minimize manufacturing costs. On the other hand, maximum and minimum pressures in main pipeline are considered as constraints which shall fall in allowed range. Therefore, a model has been developed which is a combination of a hydraulic simulation model of WTS and an optimization model based on genetic algorithm. This model is first applied to WTS of Dehgolan-Ghorveh plain as a case study. Results of this research demonstrate that based on suggested model, negative wave creation and pressure increase in pipeline is prevented as well as decrease in manufacturing costs of air chamber.

This study focuses on the reliable parametrization of the full Soil Water Retention Curve (SWRC) from saturation to oven-dryness using high resolution but limited range measured water retention data by the Hydraulic Property Analyzer (HYPROP) system. We studied the performance of five unimodal water retention models including the Brooks and Corey model (BC model), the Fredlund and Xing model (FX model), the Kosugi model (K model), the van Genuchten constrained model with four free parameters (VG model), and the van Genuchten unconstrained model with five free parameters (VGm model). In addition, eleven alternative expressions including Peters&ndash, Durner&ndash, Iden (PDI), bimodal, and bimodal-PDI variants of the original models were evaluated. We used a data set consisting of 94 soil samples from Turkey and the United States with high-resolution measured data (a total of 9264 measured water retention data pairs) mainly via the HYPROP system and supplemented for some samples with measured dry-end data using the WP4C instrument. Among unimodal expressions, the FX and the K models with the Mean Absolute Error (MAE) values equal to 0.005 cm3 cm&minus, 3 and 0.015 cm3 cm&minus, 3 have the highest and the lowest accuracy, respectively. Overall, the alternative variants provided a better fit than the unimodal expressions. The unimodal models, except for the FX model, fail to provide reliable dry-end estimations using HYPROP data (average MAE: 0.041 cm3 cm&minus, 3, average r: 0.52). Our results suggested that only models that account for the zero water content at the oven dryness and properly shift from the middle range to dry-end (i.e., the FX model and PDI variants) can adequately represent the full SWRC using typical data obtained via the HYPROP system.

In this paper, for the first time, a robust artificial intelligence (AI) technique called “outlier robust extreme learning machine (ORELM)” is used to estimate the discharge coefficient of labyrinth weirs. At the beginning, the number of the hidden layer neurons initials from 5 and continues to 45 and the most optimal number of the hidden layer neurons are taken into account equal to 5. Then, the numerical model is verified by the k-fold cross-validation approach in which k is considered equal to 5. After that, different activation functions are evaluated to detect the most accurate one for the numerical model. Subsequently, six different ORELM models are developed using the parameters affecting the discharge coefficient of labyrinth weirs. Also, the superior model and the most effective input parameters are identified through a sensitivity analysis. For example, the values of R2, RMSRE and NSC for the superior model are calculated 0.943, 5.224 and 0.940, respectively. Furthermore, the ratio of the head above the weir to the weir height ( H T / P ) and the ratio of the width of a single cycle to the weir height ( w / P ) are introduced as the most important input parameters. Also, the results of the ORELM superior model are compared with the artificial intelligence models including the extreme learning machine, artificial neural network and the support vector machine and it is concluded that ORELM has a better performance. Then, an uncertainty analysis is conducted for the ORELM, ELM, ANN and SVM models and it is proved that the ORELM has an overestimated performance. Eventually, a practical formula is presented to engineers without knowledge of artificial intelligence for approximating the discharge coefficient of labyrinth weirs. Additionally, a partial derivative sensitivity analysis is carried out for this equation.

In this study the influence of water-cement ratio on the mechanical properties (compressive, abrasion, tensile, flexural strength and permeability) of Nano-silica concrete reinforced with polypropylene fibers is evaluated. The specimens contain 4% of Nano-silica, 0.30, 0.35, 0.40, 0.45 and 0.50 of water-cement ratios and 0, 0.10, 0.15, 0.25 and 0.35 percent by volume of polypropylene fibers. Other design features remained fixed in all concrete samples. The results of the experiments showed that with decreasing the ratio of water to cement from 0.50 to 0.30, all the mechanical properties of the concrete were improved. In addition, the test results showed a significant increase in mechanical properties improvement of concrete by using polypropylene fibers. Tensile strength, flexural strength and abrasion resistance of concrete increased up to 22%, 40%, and 27% respectively for 28 days age specimens. Also, considerable reduction of hydraulic conductivity coefficient to 51% indicates high durability of these types of concrete. Compressive strength had increment of 22%, 15% and 14% for 7, 28 and 90 days age specimens respectively.