کیومرث روشنگر

One of the ways to prevent creating negative pressure and cavitation in spillways is to introduce air into the flow over the spillways. Understanding the distribution of air concentration variations along the spillway is of significant importance for estimating the aeration level. This study explores the application of GPR and SVM molels in predicting air concentration. To achieve this, a dataset of 2268 laboratory experiments obtained from hydraulic models of chute spillways was utilized in the modeling process. Various input models were defined based on different combinations of measured parameters. The results demonstrate the high capability of both methods in estimating the required air concentration over the spillway. In predicting air concentration in the chute spillway under artificial aeration conditions, flow discharge (QW), longitudinal distance ratio from the end of the deflector to the channel width (L/W), and depth ratio (perpendicular to the spillway) to channel width (Y/W) significantly influenced the outcomes. Statistical indices, including R, DC, and RMSE for this case were 0.9214, 0.8451, and 1.008, respectively, in the GPR, and 0.9333, 0.8662, and 0.937 in the SVM. For scenarios without artificial aeration, the model with input parameters QW, L/W, Y/W, and ΔP (pressure difference between atmospheric pressure and the pressure under the jet) achieved the best performance in the GPR method with values of R=0.9222, DC=0.8644, and RMSE=0.914. In the SVM, the same model with values of 0.87, 0.7543, and 0.123 for R, DC, and RMSE, respectively, was selected as the superior model.

Considering the increasing use of seismic isolators in the country, which reduce the force on structural members and increase the overall displacement of the structure during an earthquake, it is necessary to create an optimal method for using this system along with reducing the overall displacement of the structure. Also, the use of damper systems reduces the overall and inter-floor movement of the structure during an earthquake. Therefore, it is essential to investigate the effect of using damping systems to optimize seismic isolators. On the other hand, the optimal use of the combination of two damper and isolator systems can be effective in reducing construction costs and repair costs resulting from earthquakes and provides structures with optimal performance. Therefore, in this article, two viscous and frictional dampers as well as isolator were used in high-rise concrete structures with 15 and 20 floors and the combination of these two systems was evaluated in the behavior of the structure. The structures are separated by lead core rubber isolators. The results were studied under three earthquake records of the nearby area. The results showed the positive effect of dampers and separators on the behavior of the structure, and reasonable results were obtained depending on the seismic records used in the article. also, showed the positive effect of the combination of separators and dampers in reducing the amount of relative displacement, roof displacement and base shear. The simultaneous use of damper and isolator in the structures caused a 35% reduction in the base shear of the structures, 47% in the maximum relative displacement and 29% in the displacement of the roof point.

One of the significant issues in hydraulic engineering is reducing erosion in rivers by using groynes to preserve soil. The purpose of this study is to investigate numerically the groynes' presence on the sedimentary flow parameters using FLOW-3D software. For this purpose, groynes were examined at angles 30°, 60°, and 90° under 10 scenarios in various discharge and hydraulic conditions. Here, the mesh block with dimensions of 5 mm was chosen as the optimal mesh block for simulating models. In addition, Large Eddy Simulation (LES) turbulence model was used for simulations. In the scenarios where two blades are used scouring is observed in the first blade, while the sedimentation phenomenon was observed in the groyne after the first groyne in the direction of the flow. In the two blades with a distance of 0.60 m from each other, 48 mm erosion occurred. The highest amount of scour is related to the arched groyne (LLeft-Q285) with 45 mm. Discharges have an increased effect on scouring, so by comparing the scenarios with one groyne (I-Q285, I-Q200, and I-Q350), the lowest amount is with the value of 5.5mm corresponding to I-Q200. The insufficient erosion in the angled groynes corresponds to the smaller angle scenario.

Seepage is a crucial factor in the design of dams as it can lead to failure if not controlled. This study delves into the numerical modeling of seepage and investigates the effects of different sealing systems on the foundation and body of earthen dams under steady flow conditions. The study explores various sealing conditions ranging from optimal to critical, which include clay blankets, drains, and clay curtains in the foundation. In general, the combined effects have been studied by combining all types of defined elements without considering their geometric dimensions. The study reveals that the different seepage reduction systems have significant effects on reducing the flow within the dam body and foundation. The trend of seepage discharge increases with height and reaches a maximum between 38 to 46 meters before decreasing again. The combination of a drain, clay blanket, and clay curtain proved to be the most effective in ensuring the dam's stability. The study shows that this combination can reduce the flow rate by an average of 83%, the seepage rate by an average of 15.5%, and the water head by an average of 9.5% at downstream of the dam. The results of this study suggest that the diversity in the type of flow seepage reduction systems has a significant effect on reducing the flow inside the dam body and foundation. This finding underscores the importance of considering the combined effects of different sealing systems to ensure the stability of the dam.

Predicting the maximum temperature changes is very important and has become increasingly important due to the many effects it has on water resources, agriculture and the environment. By forecasting the temperature, one can be aware of future changes and make the necessary arrangements to adjust its negative effects on water resources, agriculture and the environment. Therefore, modeling and forecasting the maximum temperature can be used as an important tool in the planning and management of natural, economic and industrial resources.

In this article, the maximum temperature was modeled using the Long-Short-Term Memory (LSTM) method based on Discrete Wavelet Transform (DWT) and Complete Experimental Mode Decomposition (CEEMD) methods in two different climates (humid and semi-arid). For this purpose, the daily data of maximum temperature, minimum temperature, precipitation, and solar radiation were used from 2001 to 2020 of the synopic stations located in Siyahbisheh, Amol City in Mazandaran Province and Urmia City airport in West Azarbaijan Province. It was determined that in the semi-arid region, the parameters of maximum and minimum temperature two days before, and maximum and minimum temperature one day before, as well as the minimum temperature and solar radiation of the same day, and in the humid region, the parameters of maximum temperature two days before, and maximum and minimum temperature one day before, as well as the minimum temperature and solar radiation of the same day were recognized as the superior model.

The results of the analysis of the models showed the capability and high efficiency of the method used in estimating the maximum temperature. On the other hand, the pre-processor methods improved the results. In the investigations, it was observed that the results of analysis based on wavelet transformation led to better results so that the DC evaluation criterion for the superior model in the semi-arid region of Urmia City went from 0.965 to 0.993 and in the humid area of Amol City increased from 0.926 to 0.970 and the RMSE criterion in Urmia Airport decreased from 1.943 to 0.896 and in Siyahbisheh from 2.595 to 1.648.

The results showed an increase in DC evaluation criteria and a decrease in RMSE for the synoptic station of Urmia Airport by 2.74% and 53.87%, respectively, and by 4.80% and 35.50% for the Siyahbisheh Amol Synoptic Station, respectively. This again shows that wavelet conversion has the greatest effect in improving the performance of the LSTM model and the selected models have high capability and efficiency in estimating the maximum temperature. According to the results of the sensitivity analysis, it was determined that the temperature parameter of the previous day is the most influential in estimating the maximum daily temperature for two regions with different climates (humid and semi-arid).

Pipeline network is one of the major agents of the economic growth and development of a country, which is used to transport fluid fuels, wastewaters, and any other fluids. When these pipelines meet seas and oceans, pipes are laid on the solid bed and it causes changes in the flow pattern around the pipes. In result of these changes, the shear stress of the bed under pipelines and turbulence of current will be increased, and scour will occur under pipelines and the scour hole will form and develop. These holes cause damage and failure to the pipe due to the pipe weight and oscillating loads. Therefore, it is very important to study the scour depth and effective variables to reduce scour and prevent damage. Researchers have conducted experimental and numerical studies on scour phenomenon, considering various types of flows and conditions, and have provided different relations over the years.In this research, the effect of various factors on this phenomenon in waves is investigated using Gaussian process regression (GPR) and support vector machine (SVM). To this end, several laboratory data were used and after defining several non-dimensional parameters the performance of these methods was evaluated. The result of this research demonstrated that these methods are better than experimental relations and have promising outcomes. This study has shown that an SVM model with KC, Re and  variables in wave-induced current has the best results. It is worth mentioning that the KC variable has the most significant effect on the scour below pipelines.

Underpass gates are structures in which water moves through the gate. The purpose of this research is to investigate the effect of the sill on the hydraulic characteristics of flow in sluice and radial gates numerically using FLOW-3D software. For this aim, sills with different geometric characteristics in width, thickness, and height were investigated. The results showed that the statistical indicators in the RNG turbulence model have high accuracy compared to the k-ε, k-ω, and LES models in comparison with the experimental results. The RMSE in the RNG turbulence model for the upstream water depth and the discharge coefficient are 0.0079 m and 0.0117 m, respectively, and the average percentage relative error for these parameters is 2.94% and 1.60%, respectively. The Kling Gupta Efficiency (KGE) for the optimal mesh and mentioned turbulence model is in the very good range. In the same opening in the case without and with a suppressed, the discharge coefficient with the sill is higher than without the sill. Among the investigated sills, the discharge coefficient in the semi-circular sill is higher than in the rectangular sill. Also, the discharge coefficient of the radial gate in the without and with a sill is higher than the sluice gate in the corresponding state. The results showed that the increase in the thickness of the sill leads to an increase in the shear stress of the flow and consequently the discharge coefficient decreases. The discharge coefficient of the gate with different heights of the sill is always higher than the one without the sill, but due to the constant ratio of the depth parameter above the sill to the opening, the discharge coefficient increases with the increase of the height of the sill up to a certain level and then decreases.

Iran is faced with a dry and semi-dry climate with destructive floods, droughts, and water shortages. Droughts and floods can affect the environment, economic, and social activities. Therefore, examining and predicting river discharge and planning management to control it, especially for future water consumption, is very valuable. In this study, changes in river discharge were modeled using statistical data from 2001 to 2020.Statistical data from synoptic and hydrometric stations in a semi-arid region in Urmia city of West Azerbaijan province and a humid region in Amol city of Mazandaran province were used. Out of twelve time-series models defined for the Long Short-Term Memory (LSTM) network, the best model was identified. Then, LSTM modeling was performed based on pre-processing methods of Discrete Wavelet Transform (DWT) and Complementary Ensemble Empirical Mode Decomposition (CEEMD). The results showed that the selected model has high ability and efficiency in estimating the amount of river discharge. On the other hand, pre-processing methods improved the results such that the DC evaluation criterion in the wavelet transform increased from 0.93 to 0.95 in the Nazloo River and from 0.83 to 0.90 in the Chalous River. The best evaluation results for test data using wavelet transform for the Nazloo River in the semi-arid climate with evaluation criteria of R=0.977, DC=0.954, and RMSE=0.018 were obtained. Furthermore, the results of the sensitivity analysis indicated that the discharge parameter of one day before is the most effective in daily discharge estimation.

The purpose of this study is to measure the impact of agricultural land-use change on the process of reducing the water level of Urmia Lake and accelerate its land process. The data used in this research include Landsat satellite images in the period 2000 to 2020 and statistics and information of water sources entering the lake of Urmia Lake Rehabilitation Headquarters, which are classified by SVM, Kappa Coefficient algorithms in ENVI5.3 software and Validated and then using Arc-GIS software to determine the extent of user changes. Finally, after determining the number of changes in each land use, the amount of water required for each type of cultivation was calculated based on the climatic type and soil characteristics of the city by the NETWAT model. The satellite images show that in the period, the research aimed to change the cultivation pattern from irrigated agriculture to cropland, from an area of 395 km3 in 2000 to 688 km3 in 2020. The outputs of the NETWAT model also show that due to the change in the land-use area of agricultural lands and the change in the cultivation pattern, the amount of water consumption required in the agricultural sector has almost doubled in the growth period, from 1600 million cubic meters in 2000. It reached 2900 million cubic meters in 2020. This increase in consumption needs has eliminated the surface flow of rivers and lowered the groundwater level of the basin, which is the main reason for reducing the volume of water entering Urmia Lake.

Spillways are used to transfer and discharge the excess water collected behind the dams downstream. There are different types of spillways in terms of geometry and place of use, which are used based on the topography of the region, hydraulic and hydrological conditions, etc. Based on this, in the current research, the effect of the frontal slope of a simple and blocky stepped spillway with a semi-circular crest on the hydraulic parameters of the spillway has been investigated using the Flow-3D® model and the RNG turbulence model. Three values were considered for the slope of the front area of the spillways step and the range of critical depth was between 11.9 and 14.5 cm. Numerical simulation showed that increasing the angle of the front of the step relative to the horizon, or in other words, reducing the slope of the front of the step, has increased the relative energy dissipation, so that the amount of energy dissipation increases in the spillway with a 90 degree step compared to a step with a 60 degree angle are 13.88% and 28.89% higher than the 45 degree angle stairs. On the other hand, the use of blocks has increased the consumption of energy in the overflow with a 90-degree step compared to a 60-degree angle step by 7% and compared to a 45-degree angle step by 32.24%. Also, the value of the downstream relative depth increases with the increase of the flow rate and with the increase of the downstream energy loss, the descent number of the downstream area of the stepped spillway decreases, and this decrease is about 60% more at the angle of 90 degrees than the angles were 45 and 60 degrees.

Water pollution is a major global problem that requires constant evaluation and revision of water resources policy at all levels. Dissolved oxygen (DO) is one of the most important indicators of water quality. In the present study, the water quality parameter of dissolved oxygen using intelligent Long Short-Term Memory (LSTM) method based on discrete wavelet transform (DWT) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) pre-processor methods in both temporal and spatial modes. It was investigated in five consecutive stations on the Savannah River. The results of analysis of models showed the ability and high efficiency of the method used in estimating the amount of dissolved oxygen in water. On the other hand, pre-processor methods improved the results. It was also observed in the investigations that the results of analysis based on wavelet transformation in spatial modeling reduced the RMSE error by two percent and also the empirical mode decomposition in temporal modeling by 15 percent. The best evaluation for test data was obtained using the empirical mode decomposition in temporal modeling corresponding to the previous day with values ​​of DC=0.977, R=0.988 and RMSE=0.017. Also, in the spatial modeling to estimate dissolved oxygen in the third station, it was found that the results obtained from the inputs of the dissolved oxygen parameter one day before the second station and two days before the first station have the best results.

Reducing the water level of Urmia Lake and its effects on the environment around the lake has been one of the important national and international issues and challenges in the last two decades. In accord with the studies, one of the critical factors affecting this declining trend has been the rise in harvest, especially for agriculture. Accordingly, the purpose of this study is to simulate the future status of water resources in the Urmia Lake basin, influenced by the area of agricultural land uses.

  For this purpose, Landsat satellite image data for the period 2000 to 2020 are firstly classified using the SVM algorithm in ENVI5.3 software and the classification accuracy is analyzed using the Kappa Coefficient algorithm.In the following, the statistics and information related to the change of cultivation pattern (from arable to garden) and water sources discharging Lake Urmia are calculated. In the next step, the simulation of land use changes for 2030 and 2040 is done using two LCM and CA-MARKOV methods. And finally, after determining the amount of changes in each land use, the amount of water required for agricultural affairs in the catchment is simulated using NETWAT model.

The results show that the area of two uses, irrigated agriculture and garden will increase from 1450 and 395 square kilometers in 2000 to more than 3600 and 1650 square kilometers in 2040, respectively, This will increase the amount of water Needed or agriculture from 1,500 million cubic meters in 2000 to more than 4,100 million cubic meters in 2040.

From 2000 to 2020, water consumption in irrigated agriculture has increased by 1253.05 Km2; which according to Markov's prediction method, this amount will reach 2049.54 Km2 in 2040 that raises the amount of water consumption by 1 billion and 473 million cubic meters. The gardens land use has increased by 688.02 Km2 from 2000 to 2020, and according to Markov's prediction method, this amount will reach 1276.14 Km2 in 2040, which raises the amount of water consumption by 703 million cubic meters. From 2000 to 2020, 367.06 Km2 has been added to the drayland farming, which according to the prediction of Markov method, this amount will reach 531 Km2 in 2040, which soars the amount of water consumption by 253 MCM.

Pipelines are used all around the world to transport fluids from one location to another. When these pipelines meet rivers, seas and oceans pipes are laid on the solid bed and it causes changes in the flow pattern around the pipes. In result of these changes, the shear stress of bed under pipelines and turbulence of current will be increased, and scour will occur under pipelines and the scour hole will form and develop. These holes cause damage and failure to the pipe due to the pipe weight. In case of failure of the pipe, irreparable damages will incur to the environment and there will be huge financial costs. Therefore, it is very important to study the scour depth and effective variables to reduce scour and prevent damages. Researchers have conducted experimental and numerical studies on scour phenomenon and have provided relations over the years.
In this research the effect of various factors on this phenomenon in steady current are investigated using Gaussian process regression (GPR) and support vector machine (SVM) and it is compared with the previous presented relations. To this end several laboratory data were used and after defining several non-dimensional parameters the performance of these methods was evaluated. The result of this research demonstrated that these methods are better than experimental relations and have promising outcomes. This study have shown that an SVM model with ℎ/D, D/d, Re and S0 variables in steady current have the best results. It is worth mentioning that ℎ and variables in steady current have the most significant effect on the scour below pipelines.

Water quality forecasting plays an important role in environmental monitoring, ecosystem sustainability and aquaculture. Traditional forecasting methods cannot show the non-linearity and instability of water quality well. In the present study, the water quality parameter of dissolved oxygen was modeled using intelligent Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) methods on three consecutive stations on Savanah River located in USA. For this purpose, six different flow hydraulic and hydrological parameters including water temperature, turbidity, discharge, mean water velocity, pH and specific conductivity were used daily for seven years (2021-2015) as input parameters to model dissolved oxygen. The results showed the complete superiority of the deep learning method over the machine learning methods. According to the results, the long short-term memory method for the last model, which included all parameters, in the third station with correlation coefficient, coefficient of determination and root mean square error, respectively R = 0.981, DC = 0.956 and RMSE = 0.034 for test data performed better. Finally, by performing sensitivity analysis, by removing the water temperature parameter, it was found that DC evaluation criteria decreased by 14% and RMSE increased by 100%. Therefore, water temperature was introduced as the most influential parameter in predicting dissolved oxygen in water.

Assessing and estimating sediment transport from a long time ago is one of the major issues for hydraulic and river engineers. Determining the amount of bed load carried in rivers depends on various factors, and this factor has complicated this phenomenon. Studies on different rivers show that the amount of bed load in different hydraulic and hydrological conditions is different. In addition, the physical properties of bed load particles have a significant effect on the accuracy of model prediction, On the other hand, despite the emphasis on the unreliability of experimental equations that have been extended over a specific area, unfortunately, limited studies have been conducted on temporary changes in bed load. Therefore, Investigating the predictability of this phenomenon is of great importance. In this study, we will try to estimate the bed load in gravel bed rivers using classical and intelligent methods.

Machine learning methods due to their high accuracy in predicting various issues have been noted in recent years . Therefore, in the present study, two methods of classical artificial neural network (ANN) and deep learning of long short-term memory (LSTM), which is a kind of artificial neural network with layers and amplification algorithms to improve network performance; have been used to predict the bed load of 19 gravel-bed rivers. To define suitable models for networks, the results of 10 experimental formulas in bed load prediction have been evaluated and the parameters of superior formulas have been used as the input of intelligent networks.

The results showed that all experimental formulas had very poor results; As most formulas have predicted the bed load with a Discrepancy index of r greater than 100. However, machine methods with input parameters obtained from experimental formulas have acceptable accuracy in predicting bed load. and in comparison with machine methods, LSTM method has provided more accurate results than ANN method. Finally, the model related to the parameters of Begnold formula in LSTM method with DC= 0.900 and RMSE= 0.024 for the test data is the best model obtained from this research and The average diameter of sediment particles (D50), which is a common parameter of the top three models, has been selected as the most effective parameter in predicting bed load.

Despite the very poor performance of experimental formulas in predicting sediment transport, intelligent networks with input parameters derived from experimental formulas have had good results. Also, LSTM network is more efficient than artificial neural network (ANN) in predicting bed load transfer, which indicates that Maintaining training memory during the training process and adding reinforcement layers to the network improves network performance and increases network accuracy in subsequent training.

In hydraulic and river engineering, solid load sediment play an essential role in determining river behavior and morphological control; For this reason, the assessment and correct estimation of  solid load sediment transfport from a long time ago is one of the important issues in the sciences related to river engineering and the environment. The purpose of this study is to estimate the bed load transfer in 19 gravel-bed rivers. For this purpose, first the statistical correlation trend between sediment transport parameter (bed load discharge) and hydraulic and sedimentary parameters (flow discharge, flow depth, flow velocity, the median bed material particle diameter, Froude number,…) is investigated and the bed load discharge is estimated as a univariate regression function. According to the presented results, a favorable correlation was reached between the sediment transport parameter and hydraulic and sedimentary parameters and the results showed that these simple regression relationships in most rivers had acceptable accuracy. Also, the performance of 10 experimental formulas in bed load prediction was investigated. All formulas have had very poor results. For this reason, the parameters related to the formulas that had relatively better results than the other formulas were selected and, in order to increase the estimation accuracy, once again using two kernel-based machine learning methods Support Vector Machine (SVM). Gaussian process regression (GPR) modeling was performed. The results showed that the machine methods have acceptable accuracy in predicting the bed load and the model is related to the parameters of  Begnold formula, which includes the parameters of the stream power, the average flow depth and the median bed material particle diameter, with R =0.923 and NSE =0.851 has the best results in the machine methods.

Channel design is one of the optimization issues aiming to minimize the cost of channel construction. In this definition, the amounts of sections variables, namely radius, slope side, bottom width and flow depth are calculated by minimizing cost of channels with regard to indicating the hydraulic flow constraint. In the current study, the optimal design problems are formulated in a nonlinear optimization framework with the objective function being a cost function per unit length of the channel has been expressed as the cost per unit length of the channel for lining, the depth-dependent unit volume earthwork cost and the cost of water lost. Using genetic algorithm, the parameters of four optimized channel sections including trapezoidal, rectangular, triangular and circular sections of channels were calculated. From the results obtained for restricting the flow equation in comparison with the total cost of construction of sections, circular section has less cost of construction. Using the proposed method, which has more precision and accuracy, several models with additional restriction of velocity, Froude number, and top width were considered for optimal design of the channel under special conditions. Restriction of velocity and Froude number caused increasing by %40 and %30 respectively, but restricted top width decrease about %48 of construction cost for trapezoidal, rectangular, and triangular sections. Restriction of Froude number, top width and velocity caused increasing, the cost construction of circular section.

In this research, the relationships of the discharge coefficient of the sharp-crested U shape plan form labyrinth weirs (one cycle) have been investigated experimentally and numerically. Also, from 3 groups of weirs with heights of 10, 12.5, and 15 cm and at each height, the length of different arches to the values of 40.82, 45 and 48.10 cm have been tested. The main purpose of the present study is to determine the overflow discharge from the weir by providing relationships for the discharge coefficient experimentally. Dimensional analysis was used by 𝜋 Buckingham method to extract the relationship. Three-dimensional simulation of weirs was performed numerically with equations governing the finite volume method using FLOW-3D software, then compared with laboratory results. The results of the present study show that the proposed relationship can predict discharge values with very high accuracy and an error of 4.79% in the ratio of the head-to-height weir of 0.1 to 1.2. As the length of the weirs arch increases, the flow interference increases, and the discharge efficiency decreases. Although with increasing the length of the weirs, the length of the crest increases, the weirs efficiency decreases, the maximum throughput efficiency decreases in this case, It is at 7.01%. With constant arc length and increase in weirs height, it was observed that the throughput efficiency decreases significantly, which shows a maximum volume of flow through the weirs of 4.82%.

One of the common phenomena in water and urban wastewater transport pipes is sedimentation. Due to complexity of sedimentation phenomenon and influence of various parameters, determining the governing equations are difficult and classical mathematical models are not sufficiently accurate. In this study, capability of Gaussian Process Regression (GPR) in predicting sediment discharge in circular rainwater transport pipes with smooth and rough beds was investigated. In this regard, hydraulic and sediment parameters which had most correlation with sediment discharge were determined using factorial analysis. Then, different models were developed and using three experimental data series were investigated. Then, the accuracy of the results was compared with the classical sediment transport models. The results showed the high efficiency of the intelligent GPR model in prediction of sediment discharge in rainwater transport pipes compared to the classical methods. In both pipes the model with input parameters λs, Fm, Dgr, d50/y which are relative sediment size, non-dimensional sediment size, fraud number of sediments, and total roughness coefficient, respectively, was obtained as superior model. It was also observed that pipe diameter affected the models efficiency and with increasing pipe diameter, model accuracy increased. The pipe with a diameter of 305 mm led to more accurate results. Factorial and omitted sensitivity analysis showed that d50/y was the most effective parameter in estimation of sediment discharge in both smooth and rough pipes.

Drought is one of the most important problems which affects agriculture section and water resources. Nowadays, the use of the remote sensing technique has been considered as useful tool for drought monitoring. This study aimed to predict the temporal drought using ground station and TRMM3B43 satellite data between the years of 1998-2017. Therefore, precipitation data were first converted to the SPI index, and then, using the intelligent Gaussian Process Regression (GPR) method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD), Tabriz drought was investigated. Different models were defined and the impact of different input parameters were assessed. It was observed that the rainfall amounts from the TRMM satellite in the monthly scale had a good correlation with the Tabriz station precipitation and the results of drought analysis using satellite data were almost similar with ground station data. The obtained results proved the high capability and efficiency of the applied method in predicting the SPI drought index and it was observed that time series decomposition based on the complementary ensemble empirical mode decomposition led to more accurate outcomes. The input data decomposition increased the predictive accuracy by approximately 30 to 40 percent. It was observed that in prediction of drought the climatic elements including mean monthly temperature and relative humidity, as well as SPI indexes related to the previous months, were effective and by climatic parameters eliminating, the modeling error increased up to 15-20%. Also, the results of sensitivity analysis showed that SPIt-1 is the most effective parameter in modeling.

Drought is one of the most important natural disasters affecting agriculture section and water resources. Droughts often occur in arid and semi-arid regions. Therefore, drought forecasting is necessary and plays an important role in the planning and management of water resources. So far, numerous drought prediction methods have been proposed in the literature, including time series models, regression models, probabilistic models, machine learning models, physical models, and a host of hybrid models. Although all of these methods have shown promising results in terms of improving accuracy of drought forecasts, the impact of climate change on droughts has highlighted the need for more advanced methods for predicting this event. Engle (1982)  proposed the ARCH model which can depict the variance of the time series and eliminate the heteroskedasticity caused by the constant time series variance. The GARCH model was further developed based on the ARCH model, the advantage of which is that it can use a simpler form to represent a high-order ARCH model. On the other hand, in recent years, the Meta model approaches have been applied in investigating the hydraulic and hydrologic complex phenomena. Hybrid models involving signal decomposition have also been found to be effective in improving prediction accuracy of time series prediction methods (Amirat et al., 2018). Complementary Ensemble Empirical Mode Decomposition analysis is one of the widely-used signal decomposition methods for hydrological time series prediction. Decomposition of time series reduces the difficulty of forecasting, thereby improving forecasting accuracy. Due to the complexity of the drought phenomenon and the effect of various parameters on its prediction, in this study, the capability of GPR as a kernel-based approach and also integrated CEEMS-GPR and GPR-GARCH models were assessed for drought modeling based on six-month SPI index for the three cities of Tabriz, Urmia, and Ardabil in Iran during the period 1978-2017. In fact, this study attempts to create a novel method by combining the CEEMD and GARCH models with the GPR to enhance the estimation accuracy of the six- month SPI drought index.

Prediction of flow discharge coefficient of gates is one of the essential issues in water engineering sciences. In recent years, various semi-empirical equations have been developed in order to predict the discharge coefficient of radial gates that the application of these formulas under submerged flow conditions suffered from large errors. The aim of present study is to apply robust Gaussian Process Regression (GPR) and Support Vector Machine (SVM) to predict discharge coefficient of radial gates under submerged flow conditions and compare the obtained results with well-known semi-empirical approaches. For this purpose, an extensive experimental dataset comprises 2136 data points were used to feed the utilized methods. Different combinations of dimensionless parameters were prepared and the performance of aforementioned methods were assessed. The obtained results showed that GPR method with input parameters of y0-yt/w and yt/w yields a correlation coefficient (R) of 0.983, a Nash- Sutcliffe efficiency (NSE) of 0.967 and root mean squared error (RMSE) of 0.027 and indicated superior performance compared with employed SVM and other semi-empirical approaches.

The purpose of the design of energy dissipaters is to dissipate part of the kinetic energy of the inflowing flow in order to return safely the flow to the downstream channel or river and prevent scour below spillways, chutes and sluices. However, surveys show that despite the numerous researches, there is a lack of research on the comprehensive study of hydraulic performance of stepped spillway with curve axis under downstream transition channel slope variation. Therefore, in this study, several physical models of curve axis stepped spillway with converging training walls were made and the impact of downstream transition channel slope variation on the hydraulic characteristics of this type spillway was assessed.

This study was conducted with the aim of investigating the hydraulic performance of stepped spillway with curve axis under downstream transition channel slope variation. The experiments were carried out in a rectangular flume with length of 15 m, height of 1 m and width of 2 m. The experiments were performed at different discharge rates from 0.015 to 2.1 times the design discharge and the physical model was tested under four different transition channel slopes of m=0, m=1:33, m=1:30, and m=1:27.

The results of the experiments indicate that in the converging steeped spillway with downstream transition channel slope variation, by decreasing slope of transition channel, the discharge flood in the maximum head allowed will go up. Also, it is find that in the range of H/Hd = 0.7, the discharge coefficient in the stepped physical model for all transition slopes was less than the smooth USBR model and in the range of 0.7 < H/Hd < 1. 3 there was a good consistency with the USBR model. However, with increasing the total water head, due to the spillway submergence the discharge coefficient for all transition slopes showed a descending trend and the spillway efficiency decreased in compared with the standard USBR ogee spillway. Moreover, the results showed that the models with slope of 0 and 1:33 are two models which can pass the probable maximum flood discharge in the maximum allowable height successfully But, the model dimension of physical model with downstream transition Channel slope of m=1:33 is smaller than that of m=0.Therefore, model with slope of m=1:33 can be selected as the most efficient model.

General qualitative and quantitative results of the present study are summarized as the fallowing: 1- In the converging stepped spillway by increasing total upstream head, the discharge coefficient will go up for each of the transition Channel slope (m) and until the downstream flow is at either supercritical or critical stages, the discharge coefficient is independent of variation of transition Channel slope. By contrast, at the submergence stage for the spillway, the difference in the discharge coefficient can be due to tailwater submergence occurring in the spillway. 2- Energy dissipation over converging stepped spillway decreases with increasing the discharge ratio, but model with smaller amount of transition slope (m) lead to decline more energy dissipation in higher discharge. 3- The model with slope of m=1:33 can be selected as the best model due to it’s ability to pass the probable maximum flood in the Maximum allowable head.

A stepped spillway is a hydraulic and cost effective measure to dissipate the energy of large water flow over the spillway. Due to some limitations in stepped spillways, this study is intended a plan to increase and improve the effectiveness of energy depreciation. For this purpose, the effect of the wedge-shape elements on the velocity and pressure changes over the steps, water level and energy dissipation at downstream the stepped spillway are evaluated. In this regard, several forms of wedge elements are studied with changes in wedge arrangement and the rate of discharge by using numerical model of Flow-3D, and the appropriate models from the aspect of the most energy depreciation are selected and studied in the laboratory. In the laboratory, 25 experiments were performed on 5 physical models.Numerical and experimental results show that the addition of wedge elements on the stepped spillway has reduced the velocity and water depth at downstream of spillway to about 80% and 30%, respectively, and the energy dissipation over the stepped spillway increased by about 2.7 times. Also, by drawing the distribution profiles of pressure on the edge and the floor of steps, it was observed that the negative pressure in the horizontal section turned into a positive pressure. Also negative pressure in the vertical section decreased up to 96% and positive pressure increased about 2 times. As well as, by increasing the density of the elements, the results that increase the energy dissipation are more remarkable.

weirs are one of the hydraulic and very vital structures from various economic, environmental, safety and security aspects. In addition to passing and flow measurement, they are the controlling structures in the floods. The main purpose of the present research was to predict the discharge coefficient to method of intelligent support vector machine using experimental data as well as analysis of the hydraulic performance of weirs. The geometric variables of the weirs were used 10, 12.5, and 15 cm in height, the length of different archs were 40.82, 45, and 48.10 cm. To analysis the performance, the dimensional analysis was used by the 𝜋 Bakingham method, then by intelligent method, considering different models, the effect of different parameters in determining the discharge coefficient were investigated. As the length of the weirs arch increases, the discharge efficiency decreases, and as the length of the weirs arch increases, the crest length increases. The results of sensitivity analysis also showed that by removing the Ht/P parameter, the model error rate increases up to 14% for training data series and up to 20% for test data series, and this parameter is the most effective variable in estimating discharge coefficient in in U-shaped labyrinth weirs.