رویکردهای نوین در مهندسی آب و محیط‌زیست

چکیده Objective:The problem of erosion is among the most significant topics in the field of river engineering. Every year, a large number of bridges around the world are destroyed, mainly due to the lack of hydraulic role in their design. Methods of controlling and reducing local scour include the use of roughness, collars, submerged plates and protective piles.
Material and Methods: In the present study, the effect of submerged plates in controlling and reducing scour around the spindle-shaped pier has been investigated. In this research, plates with different angles of 15, 30 and 45 degrees were used in single, two and three rows with different flow rates.
Results and Discussion:The experimental results showed that by installing 45- degree plates with 1, 2 and 3 rows, we see 23.3, 40.5 and 43.8% reduction of scouring compared to the pier without plate. As the number of rows increases, the sediment displacement and accumulation in front of the bridge pier increases, which ultimately reduces scouring. By installing three rows of plates with angles of 15, 30 and 45 degrees to the direction of flow, we see 32.8%, 39.7% and 43.8% reduction of scouring compared to the pier without a plate.
Conclusion:By increasing the angle of the plates along the stream, their effective length increases and thus increases the sediment displacement by them, which results in more sediment being transferred to the front of the base and better scour control. Additionally, the simulation using the Flow-3D mathematical model closely aligns with the physical model, yielding an RMSE of 0.0392.

چکیده Objective:  This research was conducted to investigate the stability of the riprap downstream of the flip bucket spillway. For this purpose, a flip bucket spillway model with four angles and four sill lengths was used. In clear water, riprap with four different diameters was used in experiments to measure flow depth.
Material and Methods: In all experiments, the flow strength was adjusted, and the water depth upstream of the spillway was measured immediately downstream after the flow passed through the spillway. The exit jet from the triangular launcher was then formed, followed by the formation of a hydraulic jump. In each step, the necessary variables were measured. The Froude Number in the unstable condition of the riprap, the relative diameter of the riprap in the unstable condition, and the stability number of the riprap were calculated.
Results and Discussion: This research showed that the most stability number, related to the spillway with a sill angle of 45 degrees and a relative length of 0.17, and the least stability number was observed in a spillway with a sill angle of 15 degrees and a relative length of 0.05. To this end, 8 experiments performed on the physical model were simulated in FLOW-3D, and the results were compared.
Conclusions: In the study of the physical model, an increase in the threshold length improved the energy dissipation performance of the structure. However, in the mathematical model, this increase deteriorated the performance at angles of 15° and 25° but enhanced the performance at angles of 35° and 45°. In the mathematical model, the flow projected from buckets at 35° and 45° caused a hydraulic jump further from the structure.

چکیده Objective: Groundwater quality is a main issue in most of the plains in Iran. Therefore, quality management and monitoring of water resources are of great importance.
Material and Methods: In this study, water quality parameters, including sodium adsorption ratio (SAR), total dissolved solids ratio (TDS), and electrical conductivity (EC), were predicted using an artificial neural network (MLP type), decision tree model (M5Tree), and response surface method (RSM). The quality data acquired from 96 observation wells located in Golestan province were used for model inputs, including sodium, water pH, chloride, sulfate, calcium, and magnesium. Models were evaluated utilizing three criteria: root mean square error (RMSE), determination coefficient (R2), and mean absolute error (MAE). Three different input combinations were considered to predict EC, SAR, and TDS.
Results and Discussion: The results of this study showed that the parameters Na and Cl have the greatest effect on the accuracy of the models. According to the results, the decision tree model (M5Tree) was found to have the highest accuracy in predicting EC (R² =0.997, RMSE=23.351, MAE=13.607) followed by the RSM (R² =0.98, RMSE=56.871, MAE=33.0428) and ANN (R² =0.991, RMSE=37.1073, MAE=17.279) models. However, the RSM model has a higher efficiency than the other models in predicting the SAR and TDS. According to the results obtained, the RSM generally predicts the groundwater quality parameters with relatively better accuracy.
Conclusions: The results obtained from this research showed that the models having all input parameters offered the best accuracy in predicting EC, SAR, and TDS. Also, the results showed that the Na and Cl parameters have the greatest effect on the accuracy of the prediction results for all three methods.

چکیده Objective:  This study investigates the feasibility of integration using Caspian Sea water and different fresh groundwater levels for soybean irrigation and performance evaluation of the Aqua Crop model at the Gorgan University of Agricultural Science and Natural Resources research field in the Golestan Province, northern Iran  Material and Methods:  Irrigation applications comprised irrigation at 75% of field capacity (FC) (I1), 100 % FC (I2), and over irrigation at 125% FC (I3). Experiments were conducted during two consecutive seasons. It was done by grain yield (GY), biomass yield (BY), and water productivity (WP) under varying irrigation depths and saline water regimes. Model efficiency (E), coefficient of determination (R2), Root Mean Square error (RMSE), and Mean Absolute Error (MAE) were used to evaluate the model performance. Results and Discussion: The calibration results for (GY) and (BY) were in line with observed data, with the estimation error statistics 0.97<E<0.99, 0.11<RMSE<1.51, 0.92<R2<0.96, and 0.33<MAE<1.02 t ha-1. The model prediction error in simulating the soybean WP varied from 2.35% to 27.5% for all treatments. Therefore, the AquaCrop model accurately estimated soybean yield under different saline water and irrigation levels. Conclusions: According to the results of this study, Caspian Sea water can be considered an alternative irrigation water resource in combination with fresh groundwater for soybean crop irrigation at a ratio of 14 percent in the dry years.

چکیده Objective: Drought, recognized as one of the most severe natural disasters, is characterized by a prolonged deficiency in rainfall. This study aims to analyze drought patterns in the Khorramabad region using the Mann-Kendall test. Materials and Methods: For this purpose, the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) were applied, utilizing average, minimum, and maximum precipitation and temperature data recorded at the Khorramabad synoptic station from 1999 to 2022. Results and Discussion: The results of the drought analysis indicate the occurrence of various extreme events over the study period. The findings reveal a general increasing trend in drought severity, moving towards higher positive index values, suggesting a greater prevalence of wet years. However, temporal analysis of drought variations and the Mann-Kendall trend test indicate both positive and negative shifts throughout the study period. A negative and decreasing trend has occurred in both drought indices in the Khorramabad region, which is not statistically significant at any level because SPI Z statistic is -0.8, and SPEI Z statistic is -1.6, less than 1.96. These fluctuations may reflect localized climatic changes, highlighting the complexity of drought dynamics in the region. Conclusions: This study underscores the necessity for continuous monitoring and adaptive water resource management strategies to mitigate the impacts of climate variability in Khorramabad.

چکیده Objective: the objective of this paper is to develop and compare three MLMs, SVR, GEP and ANN- for the high-accuracy prediction of energy dissipation downstream of gabion sills in spillways. Through dimensional analysis and sensitivity evaluation using the Γ-test, the most influential hydraulic and geometric parameters are identified. The performance of each model is rigorously assessed using statistical metrics to determine their predictive reliability and accuracy, with the aim of identifying the most effective computational approach for optimizing energy dissipation in gabion-structured spillway systems. Material and Methods:  experimental data from a lab flume was used. Dimensional analysis identified key parameters affecting energy dissipation. Sensitivity analysis via the Gamma Test selected the most influential inputs. These were used to train and compare three machine learning models: SVR, GEP, and ANN. Results and Discussion: the GEP model demonstrated superior performance, achieving the highest R² (0.936) and lowest errors (RMSE=0.003) in predicting energy dissipation. It outperformed both ANN and SVR. Sensitivity analysis identified four key hydraulic parameters as the most influential inputs. Conclusions: the study conclusively found the Gene Expression Programming (GEP) model to be the most accurate and reliable for predicting energy dissipation over gabion sills, significantly outperforming ANN and SVR. The four key hydraulic parameters identified were crucial for model success, demonstrating the effectiveness of this machine learning approach.

چکیده Objective: The most important climate variable, closely related to other variables, is temperature, whose changes trigger a series of chain reactions in the environment. Also temperature is a key factor influencing plant growth. Therefore, in this study, future temperature trends in the Abhar region, affected by climate change, are analyzed over upcoming periods and compared with the historical observation period. Crop yield in future and various planting periods is predicted using the AquaCrop crop simulation model and climate change models.
Material and Methods: The observation period spans from 1986 to 2010 AD, with near, middle, and far horizons projected for 2011-2045, 2046-2079, and 2080-2100, respectively. LARS-WG software is employed alongside the NorESM1-M model under RCP8.5 and RCP4.5 emission scenarios to downscale the results of the general circulation model. Additionally, a scenario file is created in this study.
Results and Discussion: Results show that the highest yield of 4.64 tons per hectare occurs on September 27, while the lowest yield of 0.65 tons per hectare is on September 16. Moving the traditional planting date from October 7 to September 27 results in a yield increase of 0.15 tons per hectare. In the distant future horizon, the maximum yield 6.39 tons per hectare will be achieved on October 27. Furthermore, sorghum yields are projected to increase in future timelines, likely due to its involvement in the C3 photosynthetic system.
Conclusions: Consequently, the average annual temperature during the near, middle, and far future periods are expected to rise by 0.26, 0.72, and 1.46 degrees Celsius, respectively. Rainfall data indicate that November rainfall has increased to 37.61 mm with an upward trend at the 95% confidence level, while March rainfall has decreased to 32.25 mm, also at the same confidence level.

چکیده Objective: Flooding is one of the most dangerous natural events worldwide, caused by a combination of climatic, hydrological, geomorphological, and geological factors. Floods can occur due to heavy rainfall, prolonged rainfall, rapid snowmelt, or dam failure. Regardless of the cause, floods lead to widespread destruction and damage to human societies and infrastructure. Given the severe risks, assessing flood hazards has become essential. Flood sensitivity maps are useful tools to analyze and manage flood-prone areas.
Material and Methods: This study aims to identify flood-sensitive zones using the Random Forest (RF) model in the Kashkan Basin, Lorestan Province. Thirteen flood-related factors and a map of past flood events were used. Of the 58 recorded flood locations, 73% were used for model training and 27% for validation.
Results and Discusion: The analysis revealed that proximity to rivers, elevation, slope, and roughness index are the most influential factors in the region's flooding. The RF model's performance was evaluated using the ROC index, which scored 0.97, indicating excellent model accuracy in generating the flood sensitivity map.
Conclusions: Flooding is driven by various environmental and human factors. Based on the flood risk prediction map, proper management strategies can be implemented to reduce damage and casualties caused by floods.

چکیده Objective: Nitrate compounds are among the pollutants of groundwater resources, and in recent years, in terms of agricultural development and human activities, their average rate has been increasing. The purpose of this study is modeling of nitrate adsorption by using SVM, LSSVM and Random Forest model.
Material and Methods: The nitrate adsorption data used in this study were first randomized and standardized and then divided into two groups of training and testing. 70% of the data were in the training group and the remaining 30% in the experimental group. Validation of model training was performed using k-fold cross-validation method with a value of k = 5 in order to prevent over-fitting of models. The parameters of Random Forest, SVM and LS-SVM models were determined using a Bayesian optimization algorithm. The objective function of the optimization algorithm was to minimize the MSE error value of the model.
Results and Discussion: Based on the results, the Random Forest model was used with the Bagging algorithm and the parameters of minimum node size, number of trees and number of variables used were equal to 2, 10 and 3, respectively. The SVM model was trained with the RBF kernel function and the parameters of Box Constraint and Epsilon equal to 2.2156 and 0.0891, respectively, along with standardization of input and output data of the model. The LS-SVM model was also trained with RBF kernel function and setting parameters and kernel function equal to 3160/3160 and 19.7891/19, respectively.
Conclusions:  Taylor diagram results showed that the stochastic forest model and SVM had a higher correlation between observational and estimated data. Therefore, based on the results, the stochastic forest model is more consistent with the observation data and predicts nitrate changes well.

چکیده Objective: This study examines the impact of surface roughness on the discharge coefficient (Cd) of Crump weirs through a comparative analysis of smooth-surface and rough-surface configurations: Material and Methods: The experimental investigation utilized laboratory-scale Crump weir models with controlled surface roughness conditions. Different roughness regimes were created by applying uniform coatings of sand and gravel particles with varying grain sizes (ks) Results and Discussion: The results demonstrated significant effects of roughness, particularly under low-head conditions. A maximum Cd reduction of 12.8% was observed with minimal upstream head (H/P < 0.2) for high-roughness conditions. The influence of roughness exhibited progressive attenuation with increasing H/P ratios, and at H/P > 0.5, the effects of surface roughness became negligible Conclusions: These findings suggest that smoother weir surfaces are preferable for low-flow precision, whereas roughness effects can be disregarded in high-flow conditions. Future research should investigate additional factors, such as slope variations and field-scale validation, to refine hydraulic modeling.

چکیده Objective: Statistical and regression models are commonly used for this purpose, but they often yield inaccurate results due to the linear assumptions inherent in these approaches. Hydraulic models, while useful, cannot always be fully trusted due to their requirement for extensive data, the potential unavailability of required data, and the possibility of human errors leading to inaccuracies in sediment simulation. To overcome these challenges, data-driven methods—specifically a subset of soft computing techniques—can be employed. Material and Methods: This study aims to evaluate and compare five methods for estimating the sediment load at the Pol Doab station on the Qarachai River in Markazi Province, Iran. The methods considered include the Adaptive Network-based Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Gene Expression Programming (GEP), Multivariate Adaptive Regression Splines (MARS), and the Group Method of Data Handling (GMDH). The performance of these models in simulating the sediment load of rivers was assessed, and the results were compared both among the five methods and with those obtained from the sediment rating curve. Results and Discussion: The statistical indicators showed the following results for each method at the station: For the SVR model: R²=0.98R² = 0.98R²=0.98, RMSE = 185, and MBE = -5.43. For the ANFIS model: R²=0.79R² = 0.79R²=0.79, RMSE = 24.3, and MBE = 1.9. For the GEP model: R²=0.98R² = 0.98R²=0.98, RMSE = 0.74, and MBE = 0.00047. In the next step, the best-performing patterns from the ANFIS, SVM, and GEP models were used as inputs for the GMDH model. The results indicated that the GMDH model demonstrated the highest performance, with R²=0.99, 0.91, 0.98 R² = 0.99, 0.91, 0.98R²=0.99, 0.91, 0.98, RMSE = 83, 24, 73 (tons per day), and MBE = 3.2, 1.8, 1.2 respectively. Conclusion: The findings suggest that the GEP model, with R²=0.98R² = 0.98R²=0.98, RMSE = 0.74, and MBE = 0.00047, outperforms the SVM, ANFIS, and GMDH models to some extent. Moreover, the results demonstrate that all five data mining methods investigated in this study provide significantly better estimates than the traditional sediment rating curve.

چکیده Objective:  Hydrological models' performance is highly dependent on the accurate calibration of multiple parameters. Sensitivity analysis can play a key role in optimizing this process by identifying the most influential parameters. The present study investigates the sensitivity of the Storm Water Management Model (SWMM) in simulating urban runoff in Shahroud, a city located in an arid region of Iran. Methods: The sensitivity of the SWMM to changes in seven key calibration parameters, including the Width factor of the subcatchment, Curve Number (CN), Manning's roughness coefficients for pervious (N-Perv) and impervious (N-Imperv)  areas, the percentage of impervious areas (Imperv factor), and depression storage depth for both pervious (Des-Perv) and impervious (Des-Imperv) areas, was quantified using the Sensitivity Factor (S) proposed by Morris. The study area was divided into 18 relatively homogeneous sub-catchments, with runoff from these areas drained through two outlets. During the sensitivity analysis process, peak discharge and runoff volume responses to these parameters were examined across seven rainfall events characterized by different intensities and durations. Results: The results showed that peak discharge (Qp) and runoff volume (RV) are sensitive to the Imperv factor(S= 0.64 and 0.93, respectively). Qp is generally sensitive to the Width factor (S=0.17) and N-Imperv (S=0.13), while showing no sensitivity to other parameters (S<0.05). Longer rainfall durations increased the sensitivity of Qp to both CN and the Imperv factor, while sensitivity to other parameters decreased. CN significantly affected both RV and Qp only during long-duration events. Conclusions: Longer rainfall durations increased the sensitivity of Qp to both CN and the Imperv factor, while sensitivity to other parameters decreased. CN significantly affected both RV and Qp only during long-duration events.