فهرست مطالب جلال شیری

One of the most critical environmental pollutants is oil contamination. This pollution affects biological characteristics as well as the physical and chemical properties of soil. Soil is a habitat for microbial communities whose abundance and diversity can be affected by petroleum hydrocarbons. Soil biological indicators including microbial respiration, are highly sensitive to environmental stresses and respond to them quickly. Microbial respiration is one of the most common biological indicators which is used to investigate the quality and health of the soil. Since petroleum hydrocarbons are toxic and persistent in soil, studying the pattern of changes in soil biological characteristics is important in effective soil management. The aim of this study was to investigate changes in the basal respiration (BR) and substarte induced respiration (SIR) of microbial communities in the presence of oil, and how petroleum hydrocarbons can disrupt microbial respiration. For this purpose, 120 samples of crude oil-contaminated soils were collected in the oil-rich area of Naft-Shahr (located in the west of Kermanshah province) which had natural and long-term oil pollution. After measuring the physicochemical properties of soil samples microbial respiration was measured by titration method.

In this research 120, oil-contaminated soil samples were used. According to the factors included in this experiment, a nested design was used to analyze the data. The test factors included locations (4 locations) and 3 different levels of oil pollution (L: low, M: moderate, and H: high). It should be mentioned that 10 replications were considered in three levels of oil pollution and a total of 120 soil samples were gathered in this study (4×3×10). The collected soils were analyzed for soil texture, pH, EC and organic carbon (OC), and carbonate calcium equivalent (CCE) using standard methods. The concentration of petroleum pollutants, were determined by the Soxhlet extractor. In order to investigate the abundance of culturable microbial population, bacterial counting was carried out in nutrient agar (NA) and carbon-free minimal medium (CFMM)+crude oil media. Basal and substrate-induced respiration were measured by the titration method. Backward regression coefficients were used in order to identify important independent variables affecting changes in BR and SIR. Finally, the results of measuring chemical, physical and biological parameters were analyzed using principal component analysis (PCA).

The experiments showed that the percentage of oil measured by the Soxhlet method for oil pollution levels (L, M, and H) were 4.03%, 9.95%, and 22.50%, respectively. The obtained results showed that basal and stimulated breathing increased with the increase in the intensity of pollution. Also, the microbial population showed a direct relationship with the increasing of the oil pollution. The highest measured BR and SIR respiration were obtained with values of 0.053 and 0.234 mgCO2/g.h, respectively, in heavily polluted soils .Multiple regression analysis of independent variables on BR and SIR showed that the most influential variable was oil percentage, which individualy explained 59% of BR variance and 72% of SIR variance. Principal components analysis (PCA) was also done and 73% of the density variance of the samples can be justified by the first two components (biochemical component and physical component).

In a summary, according to the microbial respiration results in oil-contaminated soil, the microbial population followed by microbial respiration increased with increasing oil pollutant concentration. It seems that long-term, aged and natural oil pollution has caused the selection of resistant microbial communities to the oil compounds, hence we can observe their positive response to the presence of oil compounds, and an increase in microbial respirations (BR and SIR).. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

In the present study, in order to evaluate the effect of stress periods on wheat yield gap in rainfed lands of Bostanabad, Tabriz, Sarab, Maragheh and Harris regions located in the east of Urmia Lake basin, in periods of 11 to 20 years, the water requirement satisfaction index has been used. The linear production function, which was provided by FAO, was also implemented to estimate the potential yield. According to the obtained results, in terms of water supply percentage of rainfed wheat or evapotranspiration ratio in rainfed to standard conditions, Harris with 42% has the best situation and Sarab with 23% has the worst situation. On average, the actual yield of rainfed wheat in the east of Urmia Lake basin is 976 kg/ha. The potential yield was estimated at 4216 kg/ha, indicating a significant gap between potential and actual yield in the study area. The highest regional yield coefficient equal to 0.41 was related to Harris and the lowest value was equal to 0.21 related to the Maragheh region, respectively. The results obtained from the water requirement satisfaction index indicate the existence of suitable conditions for the production of rainfed wheat in the region. However, the actual yield in the east of Lake Urmia basin is on average about 27.2% of the potential yield. This shows that the condition of rainfed lands in the east of Lake Urmia basin is not favorable in terms of the development of the production system and it is necessary to reduce the yield gap. Additionally, the evaluation of the results showed that the correlation of the water requirement satisfaction index with the regional yield coefficient is higher compared to the actual yield and this index can be used to evaluate the yield gap.

The drinking water of Bostanabad region is mostly supplied from the groundwater resources of Bostanabad Plain. The aim of this study is to select the most reasonable and suitable method for investigating the quality of this resources. For this purpose, the rate and weight of the water quality index were calculated using multi-criteria decision making methods. The quality of groundwater resources was investigated by combining entropy and AHP weighting methods with TOPSIS and WQI rating methods. Parameters of pH, TDS, Na, SO4, Cl, NO3 and F were used in the combined methods of Entropy-WQI, AHP-WQI, Entropy-TOPSIS and AHP-TOPSIS. The coefficient of determination (R2) between the average rank of the samples and the ranks obtained from each of the methods showed that the AHP-TOPSIS method has a high ability to water ranking and classification. The samples were located in the Entropy-WQI method in 2 classes of good and average, AHP-WQI method in 3 classes of very good, good, and average, Entropy-TOPSIS method in 5 classes of very good, good, average, poor, and very poor and AHP-TOPSIS method in 4 classes very good, good, medium and poor. By comparing the combined methods, it became clear that AHP was better than entropy in terms of weighting and TOPSIS was better than the classical WQI method in terms of rating. The results showed that the entropy method is caught in an error regarding the values that are similar and close to the minimum value of the parameters that have a direct relationship with entropy.

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

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

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

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

To identify eriophyoid mites, sampling was done on dominant plants of Hesar village in Meshginshahr region of the Ardabil province during summer 2020. Two families, five subfamilies, five tribes, 12 genera and 27 species were identified among which Cecidophyopsis hesariensis Lotfollahi & Hemmatzadeh sp. nov. collected on the host plant Lonicera iberica M. Bieb. (Caprifoliaceae) was new to science and the second Cecidophyopsis species found on plants of the family Caprifoliaceae. Three species including Aculus salicisincanae (Nalepa, 1925), Aceria peucedani (Canestrini, 1892) and Aculus eurynotus (Nalepa, 1894) were new reports for Iran. A list of identified taxa is provided with comments and 22 species were reported for the first time in Ardabil province.

Various factors affect the location of the scour hole formed by the falling flow. Among these factors, we can mention the tailwater depth, the height of the falling flow, the velocity of the flow, the cross-section of the flow and the amount of air entering the flow. In the present study, the location of the formation of scour holes was investigated in the hydraulic laboratory of Tabriz University. Also, the ability of artificial neural networks (ANN) and tree models (M5P tree model) in estimating the location of scour holes downstream of dams was investigated using laboratory data and the results of these two models have been compared with the multivariate nonlinear regression method. The results showed that all three methods, the artificial neural network, the M5 tree model and regression method provide relatively accurate results in predicting the location of scour hole. RMSE value for ANN=1.75, M5=3.75 and Regression=3.89, but due to providing simple linear relationships by the M5 tree model, this method can be used as a practical method to determine the location of scour hole. The analysis of the M5 tree model showed that 4 equations with different linear equations model the pattern of changes in the location of scour hole. In addition, the analysis of the laboratory results showed that the regression equations presented in the present study compared to the common method (using projectile equations) have much less error when predicting the location of scour hole. Also, the laboratory results showed that the head passing through the structure is the most effective parameter in the formation of the scour hole.

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

Accurate prediction of pore water pressure in the body of earth dams during construction with accurate methods is one of the most important components in managing the stability of earth dams. The main objective of this research is to develop hybrid models based on fuzzy neural inference systems and meta-heuristic optimization algorithms. In this regard, the fuzzy neural inference system and optimizing meta-heuristic algorithms including genetic algorithms (GA), particle swarm optimization algorithm (PSO), differential evolution algorithm (DE), ant colony optimization algorithm (ACOR), harmony search algorithm (HS), imperialist competitive algorithm (ICA), firefly algorithm (FA), and grey wolf optimizer algorithm (GWO) were used to improve training system. Three features including fill level, dam construction time, and reservoir level (dewatering) obtained from the dam instrumentation were selected as the inputs of hybrid models. The results showed that the hybrid model of the genetic algorithm in the test period had the best performance compared to other optimization algorithms with values of R2, RMSE, NRMSE, and MAE equal to 0.9540, 0.0866, 0.1232, and 0.0345, respectively. Also, ANFIS-GA, ANFIS-PSO, ANFIS-ICA, and ANFIS-HS hybrid algorithms performed better than ANFIS-GWO, ANFIS-FA, ANFIS-ACORE, and ANFIS-DE in improving ANFIS network training and predicting pore water pressure in the body earthen dams at the time of construction.

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

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

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

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

Estimation of evapotranspiration, which is one of the components of the hydrological cycle and also an effective parameter in irrigation planning, is very important, but often the limitation or lack of meteorological data prevents to use of the proposed method of FAO Penman-Monteith. In the present study, Artificial Neural Network (ANN) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) are discussed in Improvement of estimates the ETo data in the absence of data. Climatic data in this study include radiation hours, wind speed, minimum, maximum, and average air temperature, which were used as model inputs based on the FAO56-PM equation in three stances. The data were obtained from 12 synoptic stations in arid and semi-arid climates. The results of evaluating the models showed that artificial intelligence methods provide better results than experimental methods. Also, both ANN and ANFIS models provide the best results with average temperature and wind speed input data, followed by minimum and maximum temperature inputs, medium temperature inputs and irradiation hours, respectively. Between ANN and ANFIS models, the ANFIS model Showed better results.

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

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

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

Accurate estimation of reference evapotranspiration (ETo) is an important issue in agricultural engineering, irrigation scheduling, water resources management, etc. Nowadays, the physics-based Penman-FAO-Monteith model is a standard model of ETo determining as well as calibrating other ETo equations. However, the need for large amount of hydro climatologic data has limited its application, especially in regions without sufficient necessary weather data. The present study aimed at assessing the performance accuracy of empirical temperature/radiation- based ETo models in humid regions and comparing the results with random forest (RF)-based models that used the same input variables of the applied empirical equations. The obtained results showed that the Random forest models gave the most accurate results when compared to the commonly used original and calibrated empirical equations (with similar input variables) with global MAE and SI values of 0.4 and 0.008. Nonetheless, the calibration procedure could not improve the performance accuracy of the equations in some cases.

Groundwater is an important source of exploitation in arid and semiarid regions. In the Azarshahr plain, due to the reduction in annual precipitation and lack of surface runoff, groundwater resources play an important role in supplying agricultural, drinking, industrial and domestic water requirements. The aim of this study was to evaluate the quality of groundwater resources in Azarshahr plain for agricultural irrigation and to analyze the EC and SAR variations by using geostatistical methods. So, for spatial mapping of SO4, Cl, TDS, EC and SAR, the observational data from 47 wells in this region, which were measured during the years 2003-2017, were utilized. In order to select the best interpolation method, Kriging approach with Gaussian variogram and IDW methods were used. The obtained results revealed that the Kriging with Gaussian variogram showed the lowest RMSE and the highest NS values. The results of the groundwater quality classification (using Wilcox diagram) and EC/ SAR zoning showed that the groundwater quality in the Azarshahr plain is very saline (C4S1) in most of the cases, which might be linked to the higher amounts of withdrawals from the aquifer.

Evapotranspiration (ET), a major component of the hydrologic cycle, is important in water resources management and irrigation scheduling. Nowadays, due to the lack of the lysimetric data in weather stations, the ET values calculated by the standard FAO Penman-Monteith model ( ) are used as benchmark values of grass reference crop. Also, the Penman-Kimberly model is widely applied for computing the alfalfa-reference crop ET ( ). In the present study, the meteorological data from 6 weather stations located in the Sistan-Va-Baluchestan Province covering a period of 10 years were used to calculate the and values. Then, the to  ratios were computed for all six stations during the studied period. The Penman-Kimberly model at Mirjavah station had the worst result compared to other stations. The NS coefficient values for this station are the lowest (0.07) and the SI and RMSE values for this station are 0.43 and 2.48, respectively, which is the highest value among the study stations. Finally, the contributions of the energy balance and aerodynamic components on the final ET values were determined using the Penman-Kimberly model, which showed the important influence of both components on the ET process. Consequently, the use of radiation-based models e.g. Priestly-Taylor model in these stations should be carried out by special care.

Investigating groundwater level variations is very important for sustainable management and planning of water resources. In the present study, groundwater system response to the consumptions and surface water discharge were utilized as input variations of nonlinear regression and gene expression programming models to estimate groundwater table. The used data consisted of monthly consumption amounts, surface discharge rates and groundwater levels of Bam Normashir plain (Kerman province) during the period of 2002 to 2011. The cross-correlation analysis indicated that 12 lagged monthly surface discharge as well as the current monthly consumption values had the highest impact on groundwater level fluctuations. The global relationship between these three variables was obtained using the two equations produced by ordinary nonlinear regression and gene expression programming (GEP) model. For estimating the groundwater level fluctuations, the consumption magnitudes and surface discharge values were predicted using artificial neural network (ANN) and Thomas-Firing methods, respectively. Then, the values were putted in the regression-based equations to predict the groundwater level. The obtained result revealed that in the case of using observational data, the performance of GEP was slightly better than regression models (RMSE and MAE values were 0.793, 0.636 meter respectively), while by use of the data produced by employing Thomas-Firing and ANN, the regression models gave a promising result with RMSE and MAE values of 1.437 and 1.118 meter, respectively.

The piano key weirs (PKWѕ) are type of nonlinear weirs which can increase the discharge for a given width without the increase in head water .In piano key the length of weir crest in both upstream and downstream can be incresead and therefore can be considered to be a suitable substitution for the labyrinth weirs, with a relatively small footprint In this study, in order to evaluate the Hydraulic performance of PKWs of free & submerged flow, totally 554 experiments are conducted on 11 physical models. Results showed that in the free flow the PK₁.₄ (inlet over outlet key width; wi /w₀=1.4) in the head water ratio of 0.5≤H₀/P≤1 and the PK1.25 in H₀/P≥0.1 as compared with other weirs, has higher Hydraulic performance. In the case of constant values for the wi/w₀ ratio and changing of the floor slope from 1:1.5 to the curve (a quarter of the circle; PKC) the discharge coefficient of these weirs increased about 3 percent. In the condition of submerged flow the value of the submerged upstream head over the free flow of upstream head (H*/H₀) increased by decreasing the submerged upstream head ratio (H₀/P). For the PK1 weir and in the submergence values lower than 0.48, the downstream depth had no effect on upstream depth of weir, and H*=H₀. The modifying of PK₁ causes a decrease the modular submergence range in this weir and occurrence of the full submergence with a little delay. Modifying of PKC₁, lead to an increase in the modular submergence range and no change in the full submergence.

Piano-Key weirs (PKW) and labyrinth weirs (LW) are nonlinear weirs that can increase the passing over flow magnitudes for a given width without increasing the water head. Arced configuration of this weir, improve the flow direction to the cycles And reduce inequality entering of flow to the various cycles. Arced labyrinth(ALW) and Arced piano key(APK) weirs are especially well used for spillway rehabilitation where larger probable maximum flow have required modification or replacement of the spillway. In the current paper, a total of 308 experiments were conducted on 15 laboratory models for analyzing the variations of APKW and ALW Hydraulic Performance through altering the geometric parameters, i.e. cycle arc angle(θ), weir height(P), crest shape, Magnification cycle(Lc-cycle/w), cycles width ratio(w/P), apex length(A) and compared with the LW, PK and linear weirs. By increasing the cycle arc angle and modifying this weirs, increased efficiency so that The average and maximum difference in discharge coefficient of ALW (θ=40o) than LW weirs are 12 and 21 percent, respectively and this difference are 25 and 40 percent between the APK (θ=40o) and PK weirs, respectively. Modified arc weirs are more efficient than their unmodified weirs. But with increasing Ht/P, values of their discharge coefficient connected to each other. Finally, by increasing the cycle arc angle, the superiority of piano key weirs are more than the labyrinth weirs.

Practical activities in controlling desertification must be based on current desertification situation and its severity. Based on this theory, in this study, the destructive ability of desertification was analyzed for the Bam Normashir and Rahmatabad (Kerman Province) plain aquifer using the IMDPA model and geographical information system (GIS). In this study, using the quantitative (water table depth) and qualitative (Electrical conductivity (EC), Cl and SAR) indicators as well as the soil erosion magnitudes in the studied locations, severity classes of degradation were evaluated. Then, the destruction map of the studied region was prepared by taking into consideration the maximum limitation in qualitative indices. Cluster analysis Ward and K-means method were utilized for testing the models. The results indicated that the desertification destructive in 90.81% and 8.61% of the aquifer is at severe hazard and very severe hazard class, respectively. Overall, 62% of the model results (IMDPA) was reported significant with clustering.

Evapotranspiration (ET), as a major component of the hydrologic cycle, is important in water resources management and irrigation scheduling. Nowadays, due to the lack of the lysimetric data in weather stations, the ET values calculated by the standard FAO Penman-Monteith model ( ) are used for grass reference crop. Also, the Penman- Kimberly model is widely applied for computing the alfalfa-reference crop ET (ETr). In the present study, the meteorological data from 6 weather stations located in the East Azarbayjan Province covering a period of 10 years were used to calculate the and ETr values. Then, the ETr to ratios were computed for all six stations during the study period. Finally, the contributions of the energy balance and aerodynamic components on the final ET values were determined using the Penman- Kimberly model, which showed the important influence of the both components on the ET process. Consequently, the use of radiation-based models e.g. Priestly- Taylor model in these stations should be carried out by special care.

One of the most important issues in groundwater resources quantitative management is estimating water table level using observation wells network data. The purpose of this study is to estimate the groundwater level using the combination of the geostatistics and soft computing methods. Bam Normashir and Rhmtabad plains (Kerman province) with an area of 1928 km2 was selected as a case study of this work. In this study, Kriging and IDW methods were used along with the data driven ANN, ANFIS and GEP models for predicting the spatial distribution of groundwater level, then, the best model was selected for further sampling in the studied region. Data from 65 wells during the period of 2002 to 2011 were used. RMSE, R2, AARE and MAE statistical indices were used for comparing the applied models. Results showed that for all of the models with two input parameters (including longitude and latitude), ANN and IDW models presented the most accurate results with the lowest RMSE (7.138 and 15.062m, respectively) and AARE (33 and 44%, respectively), and the highest R2 (0.606 and 0.596, respectively) for the point and regional estimation of groundwater table level. Finally, ANN-IDW hybrid model was selected for estimation and zoning the groundwater level for the future investigations.

Evapotranspiration، as a major component of the hydrologic cycle، is important in water resources development and irrigation planning. This paper aimed at investigating the abilities of Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate daily reference evapotranspiration (ET0). The daily climatic variables such as air temperature، relative humidity، wind speed and solar radiation from two weather stations (Salvatierra and Zambrana) in Spain equipped with electronic sensors for collecting of climatic data، were used as inputs to the Neuro-Fuzzy model to estimate ET0. Comparisons were made among the estimates provided by the ANFIS، Artificial Neural Networks (ANNs) and following the empirical models: Hagreaves – Samani، Ritchie، Makkink and Turc. The comparisons revealed that the ANFIS models (with RMSE between 0. 276-0. 437 mm) could be employed successfully in modeling evapotranspiration process. The ANNs (with RMSE between 0. 298-12. 5 mm) were also found to perform better than the empirical models in this regard.

In order to determine and analyze the head losses caused by the combined effect of entrance resistance and approach flow convergence to drains, some known models were studied. The field data of an existing sub-surface drainage system were applied for evaluating the models and selecting the best one. The hodograph solution was employed to compute the water head, directly above the drain, while the Oosterbaan model was used to validate the hodograph results. Total head loss between two drains, was determined using Ernst equation. The results showed that increasing total head loss caused reduction of hydraulic head in approach flow region in a non-linear manner. Application of Youngs and Hoffman equations as well as EnDrain program based on energy and Darcy equations could simulate drain discharge to some extent. Hoffman equation gave some promising results among the others in term of R (0.928), RMSE (0.0007) and SI (1.09).