فهرست مطالب masoud karbasi

A spillway is a structure used to provide the controlled release of water from a dam or levee downstream, typically into the riverbed of the dammed river itself. One of the types of spillway used in dams is vertical shaft spillway or the same as morning glory spillway. One of the methods to increase the flow rate and reduce the occurrence of vortex currents in the spillway is to modify the shape of the inlet of the spillways. In recent years, spillways with daisy, piano key and zigzag inlets have been proposed known as spiral spillways. A review of the results of previous research shows that the common goal in the study of vertical shaft spillways is to pass the flood flows with the highest possible discharge and the lowest water level and reducing the eff destructive effects of vortex currents. In previous researches, issues such as the effect of the inlet type of vertical shaft spillway and geometric parameters of the structure on the discharge coefficient and the study of vortex occurrence conditions and hydraulic flow of vertical shaft spillway. In this research a new shape of vertical shaft inlet in form of Bow-tied spillway was introduced and discharge coefficient of these spillways was investigated using a series of experiments.

The experiments of this research were performed in a metal cube tank with 1.5 m length, 1 m height and 1 m width with transparent Plexiglas side walls in the hydraulic laboratory of Department of Water Engineering at Zanjan University. Bow-tied spillways were examined with different angles of the bow-tie (60-150 degree) in both cases of with middle arc and without middle arc and the results were compared with a morning glory spillway. In this research, by preparing eight bow-tie spillways and a morning glory spillway with a total length of 60 cm and considering the hydraulic conditions (H/D), 72 experiments were run to investigate the effect of the geometric shape of the spillway on the discharge coefficient.

Results showed that in Bow-tied models without middle arc, increase of the bow-tie angle leads to reduce the flow interference and increase of flow discharge over the spillway. So that B-150 model (with 150 bow-tie angle) had higher discharge coefficient than the other models of bow-tied. This model had better hydraulic performance than morning glory spillway (10 percent). Also, economic advantage of this model was higher than morning glory spillway (31 percent). Results of the tip position of the bow-tie spillway show in tip angle of 120 degree, the tip position of the bow-tie has a significant effect on the discharge coefficient of the spillway, but with increasing the bow tip angle from 120 to 150, the tip position of the bow-tie spillway did not have much effect on the discharge coefficient. In Bow-tied models with middle arc, change of bow-tie angle had no significant effect on discharge coefficient and hydraulic performance of these models was lower than morning glory spillway (5.3-9.7). This could be due to the interference of fall currents and the low internal area of these models.

Comparison of the hydraulic performance of bow-tie models with morning glory spillway shows that the B-150 model has the highest hydraulic performance (10%) compared to other models. On the other hand, this spillway has a better economic advantage (31%) than the morning glory spillway. In the construction of reservoir dams, especially earthen dams, the major part of the executive cost is related to the implementation of concrete structures of emergency spillway. In this situation, by choosing Bow-tied spillway with optimal geometry, a suitable economic savings can be achieved along with improving the hydraulic conditions of the flow.

One of the most important and practical physical parameters in studies of soil water flow is Soil Water Characteristic Curve (SWCC). Measuring the soil moisture characteristic curve through the direct method is expensive and time-consuming. For this reason, a variety of indirect methods including intelligent models have been developed. In this study, the performance of three models included multilayer perceptron neural networks (MLP), cascade neural network (Cascade-NN) and gene expression programming (GEP) were evaluated and compared to estimate of SWCC. The measured data from 108 soil samples, including soil particle size distribution, soil moisture in different suctions and the bulk density were used. In all models, three different input data combinations were used. Comparison of predicted and observed values of soil moisture showed acceptable performance of all three models, however, the Cascade-NN neural network model was relatively superior. The R2 values of test phase for the best structure of the neural networks (MLP), neural networks (Cascade-NN) and gene expression programming (GEP) were 0.95, 0.96 and 0.93, respectively, and the RMSE values were 3.74, 3.25 and 4.10 %, respectively. Comparison of the results of different input data scenarios indicated the low accuracy and difference between the results of the models in the first scenario, but adding the parameters of porosity and moisture at field capacity point to the input data in the second and third scenarios, increased the accuracy and difference between the results achieved by the models. Finally, it can be emphasized that the cascade-NN model was introduced as the superior option, using all the mentioned physical data.

Drought is one of the most important and damaging natural disasters in the field of water resources that occurs in all climatic regimes of the country. Therefore, predicting and dealing with it is very important. In the present study, 79 synoptic stations in Iran were selected as the study. Three meta-heuristic optimization algorithms TLBO, IWO, PSO and the conventional Levenberg-Marquadt algorithm were used to train the multilayer artificial neural network to predict the SPEI12 drought index for the next one to three months. Due to the large number of synoptic stations, the stations were divided into five clusters C1 to C5 according to the time series of the drought using the K-means method. The results were compared with respect to the location of the stations in the clusters and the accuracy of the models was evaluated based on the RMSE and R2 indices of the test data. Showed that in all three prediction models, the accuracy of the models decreased with increasing prediction time. Comparison between the three optimization algorithms mentioned and Levenberg-Marquadt algorithm as a widely used algorithm in optimizing neural network weights, showed the better performance of meta-heuristic algorithms. The comparison between the three TLBO, IWO and PSO algorithms showed that the TLBO algorithm performed slightly better than the other algorithms and provided more accurate results. R2 was observed in cluster one (eastern regions, southern strip and southeastern regions of Iran) and the highest RMSE values and the lowest accuracy of the models were observed in cluster five (northern strip strip of the country).

Drought is one of the most important natural hazards and often affects the lives of many people and causes economic damage, environmental damage and social problems. This study was conducted to evaluate the drought trend, intensity and duration during 1988-2019 in relation to the existing climates in the southern Alborz region. To describe droughts in 3, 6, 12 and 24-month time scales, two drought indices, namely standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) were used. In the case of SPEI, evapotranspiration was an important factor in monitoring drought. Therefore, (HS) method was used to calculate evapotranspiration. Drought intensity and duration maps were presented for SPI and SPEI. The correlation between SPI and SPEI was also examined. In the present results, unlike humid climates, SPI does not have a good correlation with SPEI in extremely arid and arid climates and shows the importance of reference evapotranspiration (ET0) to identify drought in these areas and the index (SPEI) has more accurate monitoring results. Relative to the index (SPI). In addition, drought intensity maps for (SPI) and (SPEI) have shown that the region is experiencing very dry conditions and water resources are affected by intense drought in the short and medium term. Short-term drought in the east of the region (Zanjan) and long-term drought in the center and west of the region (Garmsar, Semnan, Karaj, Tehran) have been more.

Rainfed cultivation is one of the best ways to rich the sustainable development with optimal use of green water. About 75 percent of the world's cultivated areas are rainfed, which forms important part of the international economy. For a long time rainfed cultivation has been a common method in low-rainfall areas of Iran and mentioned method is one of the main ways of producing crops yet. Rainfall is an important factor in rainfed agriculture, so its limitations and non-conformity with planting season can reduce the production efficiency. Wheat growing period consists of three seasons of the year. Rainfall in spring, fall and winter is effective on wheat growth. Cereal grains, especially wheat, are the most important food source in the world. After cereals, legumes are considered to be the main human food source and have significant nutritional and agricultural importance. In addition to being high in quality and valuable, suitable and complementary protein for cereals in the nutritional pattern, lentils are also among the legumes that stabilize the air nitrogen in the soil, which lends itself to crop rotation. Thus, intermittent cultivation of rainfed wheat with rainfed lentil is an important factor in stabilizing production in the developing countries. Given that Iran is in arid and semi-arid regions and its population is growing, it is important to plan, and evaluate the past in order to predict the future. Zanjan province is the fifth country with 6.9% of total grains production. In this study, the effect of precipitation in water requirement of these two important products and their water stress has been studied in the climatic conditions of Zanjan plain. Zanjan province is located at the longitude of 47º 10' to 50 º 5' East and the latitude of 35º 25'37º10' North. The area of Zanjan province is about 39369 square kilometers. According to the De Marten climate classification, the tropical, temperate and subtropical climatic zones can be identified at the applied level. Average rainfall over the past decade has been reported as 301 mm. For conducting this study, statistical data including meteorological and crop yield data 2004-2014 obtained from the Meteorological and Agriculture-Jahad Organization, respectively. Zanjan synoptic station with the latitude and longitude of 48º 31' East and 36º 39' North, respectively, is located 1663 meters above the sea level. In the last 50 year, the annual precipitation trend has been decreasing and this trend is affecting the water availability of rainfed plants in the region and can decrease yield of crops. Although in the long term the average temperature trend is slightly constant, the yield has also declined with decreasing rainfall. Regarding the importance of wheat and lentil in arid lands of Zanjan plain, these two main rainfed crops were investigated in this study by determining the regression relationship between yield and evapotranspiration with precipitation and water stress coefficient. The results showed that the average evapotranspiration values of wheat and lentil for Zanjan plain during the study period were 398 and 262 mm and the mean temporal stress coefficients were 36 and 33%, respectively. Regression analysis showed the highest correlation between actual evapotranspiration and yield. The highest correlation was observed between temporal stress coefficient and yield of rainfed wheat. But, in lentils this trend was different and the correlation between water stress coefficient (Ks) and yield was more than the precipitation and lentil yield. For 25% decrease in precipitation, wheat yield reduced almost 18%. Generalizing this issue, the response to water stress coefficient of the product or Ky become 1.14. The regression equation also indicated that in case of being precipitation about 300 mm, wheat yield in the area will be near one tone. For 25% decrease in precipitation, wheat yield reduced about 22%. According to figures, the fitted model was significant and well-acted; and during the growth period of these crops, lack of water supply caused water stress as well as severe yield loss. The occurrence of high tension time percentage of 25% for wheat and 20% for lentil in all years of the study period confirms that the climatic potential of the region does not reach the standard requirements of plants, so it is not possible to achieve optimal performance. Hence, new methods of tillage, conservation agriculture and supplemental irrigation are recommended for achieving high yield, as well as the expected cultivation method for crops.

Increasing water demand and water pollution due to the development of agricultural, urban and industrial activities have caused environmental problems all over the world. The significant increase in water pollution and the diversity of various urban, agricultural and industrial pollutants made the qualitative management of water resources inevitable. Short-term and long-term accurate forecasts of river quality parameters are essential for designing hydraulic structures, irrigation planning, optimal utilization of reservoirs and environmental planning. Given the stochastic characteristics of the hydrological events, forecasting the future status of surface waters is always associated with uncertainties. The purpose of the present study was to investigate the performance of two types of artificial neural networks, namely MLP and GMDH, combined with discrete wavelet transform (DWT), to forecast two important quality parameters, electrical conductivity (EC) and sodium adsorption ratio (SAR) at Zayandeh-Rood River in 1, 2 and 3 months ahead. Material and In this study, water quality data (EC and SAR) of Zayandeh-Rood River at Zaman Khan Station was used from 1363 to 1384. From 21 years of data, 15 years (approximately 70%) were used for training and 7 years (30%) were used to test the developed models. Two types of mother wavelet dmey and db4 were evaluated. Statistical parameters such as RMSE and R2 were used to evaluate the performance of the models. The results showed that the use of discrete wavelet transform improves the performance of the models. Various combinations of input data (various delays) and two types of mother wavelets were evaluated. The results showed that wavelet-MLP and wavelet-GMDH hybrid models outperform single MLP and single GMDH models at all forecasting intervals. The results of the single MLP and GMDH models were only effective in forecasting SAR one month ahead but practically could not forecast two and three months later. In the EC parameter, the MLP and GMDH models performed better. Also, the results showed that the use of annual time lags does not increase the accuracy and in some cases even reduces it. The study of the types of mother wavelets also showed that the dmey wavelet is the most suitable wavelet type to forecast EC and SAR qualitative parameters. The comparison between wavelet-MLP and wavelet-GMDH models showed the relative superiority of the former model. By increasing the forecast period from one month to three months ahead, the accuracy of the models decreased. This decrease in precision was higher in forecasting SAR parameter, e.g. in the one month forecast, R2 was 0.936 and in the 3 months ahead forecasts it was reduced to 0.516. In the EC parameter, the R2 fell to 0.641 in 3 months ahead forecasting. The results of this study can be used as a basis for future planning for water quality. It is suggested that the model presented in this study should be considered in other rivers. Also, the combination of other artificial intelligent models such as ANFIS and SVM with wavelet transform can be evaluated.

The bed forms or in the other word, bed irregularities are structures that form due to stream flow and they have direct impact on roughness and flow resistance in sand bed rivers. Bed forms have different shapes and forms in sand bed rivers. Since river discharge and flow velocity are totally affected by roughness, accurate prediction of the shape of the bed is of great importance. Due to the influence of different parameters in the formation of the river bed form, it is difficult to determine the governing equations and the mathematical models with sufficient precise. Today, the use of artificial intelligence systems has expanded as a new way of analyzing water resources issues. The objective of this research is to introduce a method that can be used to predict the shape of the river bed with high precision. In the present study, the data were randomly divided into two parts of the training (70%) including 1647 laboratory data and test (30%) containing 560 laboratory data. The decision trees were coded on the data of the test section in the WEKA programming environment, and finally calibration was performed on the data by using Random Forest and Random Tree algorithms. Then the experimental methods of Van Rijn, Engelund and Hansen and Simons and Richardson were implemented on test data. Evaluation of the results were done using root mean square error (RMSE), Correctly Classified Instances and Roc area under curve. The results showed that the best performance reached by Random Forest algorithm for experimental data with statistical criteria of CCI=85 (%), RMSE=0.17, ROC=0.97. On the other hand, by examining the results of empirical methods, it was determined that for laboratory data, van Rijn method has better performance with the results of CCI=64 (%), RMSE=1.07. Between different variables, discharge, width, depth, slope of the channel, average diameter of sediment particles and temperature for laboratory data were the most important parameters in predicting bed forms. In this research, the superiority of soft computing models was evident compared to the empirical methods in modeling and predicting of the bed form. Models performed better in the WEKA environment.
Basically, because of the formation of the river bed form is depended on several factors, and also because of its complex nature, the prediction of this phenomenon is very difficult and sometimes with high errors. Since artificial intelligence methods are used to analyze issues that do not explicitly describe the nature of the problem, so many of the issues of bed form can be solved with these methods.

It is important to study the coefficient of resistance in streams, especially in open channels, canals and rivers. One of the factors influencing the flow resistance is bed-forms. Cluster microforms are types of bed-forms in mountainous rivers, which are important both in biological and in hydraulic as well as in the secondary currents. Study on the recognition and influence of clusters on flow resistance is novel. The purpose of this study is to investigate the effect of cluster shape and particle size of clusters on the flow roughness coefficients.
In order to investigate the effect of the shape and size of the particles that forming clusters, experiments were carried out in a laboratory channel of 20 meters in length, 0.6 m in width and 0.6 m in height. Using gravel particles with three different sizes of 9.5, 12.5, 15.5 mm, cluster three cluster types (linear, heap and rings) were constructed in laboratory flume. Two roughness coefficients namely Darcy-Wiesbach and Manning were calculated using the water surface slope measurements.
The results of this study showed that the linear cluster has the least effect on the rate of flow resistance coefficient. The ring and heap clusters have a roughness coefficient greater than the linear cluster, but both have almost same impact on the flow resistance. The results of these two forms of the cluster are very close for particles of 9.5 and 12.5 mm, but for a particle of 15.5 mm, the heap cluster coefficient of roughness is higher than the roughness coefficient of another. The manning’s roughness coefficient is also increased by increasing the diameter of the gravel particles of the cluster builder. By performing experiments with gravel particles of different diameters, the percentage of change in roughness coefficient relative to the non-cluster state for cluster clumps in particles of 9.5, 12.5 and 15.5 mm was 47, 52 and 75, respectively. For rings and particles of 9.5, 12.5 and 15.5 mm, the percentage changes were 48, 49 and 75 percent, respectively, and the percentage for linear clusters for particles of 9.5 and 19 percent, and particles 5 / 12 and 15.5 mm were observed at 37 and 67% respectively, indicating an increase in the flow resistance with an increase in the particle diameter. Also, the results showed that increase in Froude number would decrease the roughness coefficient.
The results of the experiments made it clear that the cluster affect the flow resistance by increasing it. The results of this study showed that the heap cluster has the most effect on flow resistance.

Local Scour phenomenon in downstream of hydraulic structures such as spillway, gate, stilling basin is one of the most important issue. Scour around the structures in addition to an important impact on the stability of the structure, may change the flow characteristics and the result of changes in the structural design parameters. Reveals the importance of scouring time that the scour depth is considerable, so that the depth of the river is the foundation structures and stability of these structures are at risk. If the maximum scour depth is not anticipated, it creates a serious problem in design. Hydraulic structure designers arent able to provide the necessary measures to reduce damage caused by scour. Careful examination of the claim requires more tests and more accurate. The aim of this study is to investigate the effect of Bentonite clay in the form of the bed scouring.
For this purpose, 87 tests were conducted in different laboratory conditions on flume of Hydraulic laboratory which Situated in Research Institute of Soil Conservation and Watershed Management. Studies were conducted on 29 tests of non cohesive materials with average of 0.2 mm diameter as an evidence and 58tests on materials with a viscosity of 2%, 5%, 8% and 20%. three crescent gate with 5,7.45,10.82 cm diameters, with the different tail water within 5 cm to 21 cm and four gate openings of 1,2,3 and 4 centimeters, three discharges about 1 to 5 liters per second.
To measure tail water depth and measure flow discharge used a standard sharp edge triangular weir with 90 degrees.
By examining the longitudinal profile of scouring was found dimensionless scour hole profiles and hills created with three curve edge sluice gates diameters 5, 7.45, 10.82 cm and a simple slider, That retains its shape over time and They almost overlap and the curves are similar and stylish. It is worth mentioning scour profiles that were created after curve edge sluice gates was observed secondary hill in the ratio of tail water to opening of gate size 3, 5, 9 but the ratio of more than 10 in its impact will be low enough. As a result of scour profiles behind the slide gate is simple and there was no secondary hill. With using point gage took hole dimensions and the diagram of the scour hole was presented with software surfer. The results showed that the addition of Bentonite clay minerals is reduced scouring hole size. It was also observed an increase of 5% Bentonite clay is reduced 80.61 percent and about 8 percent rise is reduced 84.82 percent scour depth. Dimensionless relationship was presented for effective parameters. Logarithmic line fitted with the interpretation is fairly good correlation with a correlation coefficient of 0.76.
Studies in this research showed that the crescent gate has impact on the scour hole, and in the majority of cases examined scour profiles after the crescent gate can be seen secondary hills

Reference evapotranspiration is one of the most effective components of agricultural water use and management of water resources. Determination of the water requirements of various plants during the growing season is necessary in order to prevent water waste and proper planning (7). In recent years the use of artificial intelligence technics and hybrid model in forecasting of hydrological parameters has become very popular (12). The objective of this study is to evaluate GMDH neural network and wavelet-GMDH hybrid models in forecasting of daily reference evapotranspiration at Ahvaz synoptic station.
For this purpose, 10-year period (2000 to 2009), 7 years (2000-2006) for training and 3 years (2007-2009) to test different models were considered. Reference crop evapotranspiration time series generated using standard penman-monteith equation. Different combinations of inputs (different delays) and various mother wavelets were examined. To test different models were considered different combinations of inputs (9 different delays); and different mother wavelets (13 mother wavelets). A total of 126 models 117 of them related to hybrid models wavelet- GMDH and 9 for GMDH neural network were carried out. To choose the best model, statistical criteria such as coefficient of determination (R2), root mean square error (RMSE) and the mean percentage error (MARE) was used.
The results showed that wavelet-GMDH hybrid model (RMSE = 0.31 mm / day) compared with GMDH neural network (RMSE = 1.22 mm / day) has higher accuracy in forecasting daily reference evapotranspiration. The results showed that use of delays longer than four days have little effect on the accuracy of models and in some cases can result in reduced accuracy. The results of similar studies that have used wavelet transform to preprocessing data are in correspondence with our findings (1, 4, 5 and 12). Results of GMDH neural network showed that number of delays did not affect the accuracy of model. This study evaluates the accuracy of the wavelet-artificial neural network hybrid model for different mother wavelets. Results showed that Meyer mother wavelet due to its complexity and its shape improved the accuracy of models. These findings correspond with the findings of Rajaee and Ebrahimi(2014), Shoaib et al(2015) and Toofani et al (2012) (13, 15 and 17).
Results of this study showed that, at the forecasting of one day ahead reference evapotranspiration Wavelet-GMDH model (MAPE=5.53%) has significant superiority to GMDH model (MAPE=22.11%). The results of this study can be used in the planning of irrigation water in this area. At the end it is recommended that proposed models has been evaluated in different climate conditions of Iran.

Accurate estimation of crop evapotranspiration (ETc) plays an important role in improving the water useefficiency (WUE) and water management. Due to the presence of different types of coatings plant in large areas, the accurate calculation of the water requirements by conventional methods is elusive or impossible. Remote sensing methods are becoming attractive to estimate crop evapotranspiration, as they cover large areas and can provide accurate and reliable estimations. Present study estimates the actual evapotranspiration of corn and alfalfa cover at Khoramdareh region using remote sensing and meteorological data. Surface energy balance algorithm for land (SEBAL) and Landsat 8 images were utilized to determine actual evapotranspiration of corn and alfalfa covers. The obtained results were compared with the results of the PM-FAO56 model. The results showed that two models had good agreement with each other. Values of RMSE for the estimation of actual evapotranspiration of corn and alfalfa was found respectively, 0.92 and 1.25 mm/day. The determination coefficient (R2) for corn and alfalfa was respectively 0.89 and 0.83 respectively. The results showed that the SEBAL algorithm estimates the actual evapotranspiration of corn and alfalfa crop respectively less and more thanPM-FAO56 model.

Estimation of reference crop evapotranspiration is one of the most important elements in optimizing agricultural water consumption and in management of water resources. Forecasting the daily and weekly reference evapotranspiration can be used in predicting of crop water requirements and in short range planning irrigation. The aim of this study was to evaluate the performance of three types of artificial neural networks: MLP (multilayer perceptron), RBF (radial basis function network), and SVM (support vector machine) in forecasting the daily and weekly reference evapotranspiration at Tabriz synoptic stations. For this purpose, the meteorological data of 39-year period (1971-2009) were used. To train the neural network, 80 percent of time series data was selected randomly and 20 percent of data was used to validate the different models. To create the time series of daily and weekly reference evapotranspiration in a given period, the standard Penman-Monteith FAO 56 equation was used. Different combinations of input data (different delays) were used to evaluate the models. The results of daily forecasting of reference evapotranspiration showed that SVM with RBF kernel with input set of M5, RMSE=0.51 mm/day and R2=0.92 had the best performance. Moreover, the results of weekly forecasting of reference evapotranspiration showed that SVM with polynomial kernel with inputs set of M8, RMSE=3.88 mm/week and R2=0.95 had the best performance.