Journal of New Approaches in Water Engineering and Environment

Abstract 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. 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. 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 plate. 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.

Abstract 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.

Abstract Groundwater quality is a main issue in most of the plains in Iran. Therefore, quality management and monitoring of water resources is of great importance. In this study, water quality parameters including sodium adsorption ratio (SAR), total soluble solids ratio (TDS) and electrical conductivity (EC) were predicted using artificial neural network (MLP), 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 are sodium, water pH, chloride, sulfate, calcium and magnesium. Models were evaluated utilizing three criteria of root mean square error (RMSE), detemination coefficient (R2) and mean absolute error (MAE) were used. Three different input combinations were considered to predict EC, SAR and TDS. 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 followed by the RSM and ANN models. However, the RSM model has a higher efficiency than the other models in predicting the SAR and TDS. According to the obtained results, it can be said that the RSM in general predicts the groundwater quality parameters with relatively better accuracy.

Abstract the aim of this study was feasibility of integration of saline water and fresh groundwater for soybean irrigation and Performance evaluation of AquaCrop model at the research field of Gorgan University of Agricultural Science and Natural Resources in the Golestan Province ,northern Iran. For this purpose, the AquaCrop model with salinity module (FAO-2012) was calibrated and validated with soybean crop data of 2013and 2014, by grain yield, biomass yield and water productivity under varying irrigation and salinity regimes. Irrigation applications comprised irrigation at 75% of field capacity (I1); at 100 % FC (I2); and over irrigation 125% FC (I3). The salinity application levels were fresh water with Electrical conductivity about 0.86 (S1); 5 (S2) and 8 dS/m (S3).The different saline water were made with mixing a ratio of the Caspian Sea water (ECw=26.5 dS/m) and fresh water. Model efficiency (E), coefficient of determination (R2), Root Mean Square error (RMSE) and Mean Absolute Error (MAE) were used to test the model performance. The model was calibrated for simulating soybean grain and biomass yield for all treatment levels with the prediction error statistics 0.97

Abstract 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. 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. 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 which is less than 1.96. These fluctuations may reflect localized climatic changes, highlighting the complexity of drought dynamics in the region. This study underscores the necessity for continuous monitoring and adaptive water resource management strategies to mitigate the impacts of climate variability in Khorramabad

Abstract This study evaluates the energy dissipation efficiency of gabion structures positioned downstream of spillways by employing three machine learning models (MLMs): Support Vector Regression (SVR), Gene Expression Programming (GEP), and Artificial Neural Network (ANN). Utilizing 155 laboratory-derived datasets, the models were trained (70%) and tested (30%) to predict energy dissipation performance under varying hydraulic conditions. GEP emerged as the most accurate model, achieving exceptional training-phase metrics (RMSE=0.00308, MAE=0.00250, R²=0.93648), closely followed by ANN, while SVR lagged in predictive capability. During testing, GEP maintained its superiority, reinforcing its robustness, with ANN remaining competitive and SVR continuing to underperform. Sensitivity analysis via the Γ-test identified dimensionless parameters- relative tailwater depth ("y" _"2" /"y" _"1" ), gabion height-to-upstream flow depth ratio ("h" /"y" _"1" ), aggregate size-to-upstream flow depth ratio ("d" /"y" _"1" ), inflow Froude number (Fr₁)- as critical drivers of energy dissipation. These findings underscore the efficacy of evolutionary algorithms like GEP in capturing complex hydraulic interactions, attributed to their ability to evolve interpretable mathematical expressions. ANN, though less interpretable, proved a reliable alternative, while SVR’s limitations in handling nonlinear relationships were evident. The study highlights the potential of MLMs, particularly GEP, to enhance spillway design by optimizing energy dissipation, reducing erosion risks, and improving infrastructure resilience. Future work could explore hybrid models, larger datasets, and field validations to refine and broaden applicability in hydraulic engineering practice.

Abstract Temperature is one of the factors affecting plant growth. Thus, future temperature trends in Abhar region affected by climate change during future periods is evaluated and compared with the period of observation.The study observation period is considered as 1986-2010 AD, near horizon 2011-2045, the average horizon 2046-2079 and 2080-2100 horizon in the current study. LARS-WG software is used in NorESM1-M model and RCP8.5,RCP4.5 concentration pathway scenarios in order to downscale the results of general atmosphere circulation's simulation model. Furthermore, the scenario file is generated in this study. According to the obtained results, the highest yield is cultivated on 27 September with 4.64 tons per hectare and the lowest yield is produced on 16 September with 0.65 tons per hectare. If we change the traditional cultivation time from 7 Octoer to 27 September , a growth of 0.15 tons per hectare will be seen. The highest yield will be in the far horizon with 6.39 tons per hectare on the 27 of October. Also, in the future horizons, the yield of sorghum will increase. which can be considered to be involved in the C3 photosynthetic system of this plant. The average annual temperature during the near, middle and far future horizons will be 0.26, 0.72 and 1.46 degrees Celsius respectively. Also, the time series of rainfall indicates that the amount of rainfall in November increased to 37.61 mm and has an upward trend at the 95% confidence level and its decrease to 32.25 mm in March at the same confidence level.is.

Abstract 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.

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: 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.

Abstract Nitrate compounds are among the pollutants of groundwater resources that in recent years in terms of agricultural development and human activities, their average rate is increasing. This ion may enter drinking water as it passes through the ground, or it may enter groundwater sources as a result of water contamination with organic matter and the accumulation of municipal and industrial waste, or the accumulation of animal and chemical fertilizers or the leakage of municipal sewage facilities. But in recent decades, increasing use of nitrogen fertilizers has led to the addition of nitrate in surface and groundwater.

The 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 Bayesian optimization algorithm. The objective function of the optimization algorithm was to minimize the MSE error value of the model. 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.

Abstract Weirs are one of the most common hydraulic structures for measuring and controlling water surface elevation and are widely used in hydraulic engineering projects and irrigation and drainage networks. In this paper, the effect of surface roughness on the discharge coefficient (C_d) of the crump weir was studied and compared with that of the crump weir with a smooth surface. For this purpose, laboratory models of crump weirs were considered, whose surfaces were separately coated with sand and gravel of different grain sizes. Analysis of this issue based on dimensional analysis technique explained that the effect of surface roughness on C_d can be evaluated using two dimensionless parameters including the ratio of upstream head to weir height (H⁄P) and the ratio of surface roughness to flow head (k_s⁄H) by simultaneously considering the relative roughness values (k_s⁄P). The results showed that increasing the roughness at low values of upstream head causes the C_d to decrease by about 12.8 percent. However, as the upstream head increases, the effect of k_s⁄P decreases to the point where its impact is negligible.

Abstract Sediment load along with river flow causes irreparable damage to water development projects. Estimation of river sediment load is an important and practical issue in the study and design of water and hydraulic projects. The purpose of this research is to evaluate and compare adaptive neural-fuzzy models (ANFIS), (SVM), (GEP), (GMDH) and (MARS) and compare with the (SRC) method in estimating sediment load of Pol Doab station of Qarachay River, Markazi Province. For this purpose, the performance of 5 types of data mining models in simulating river sediment load was investigated, then the results of the 5 methods were compared with each other and with the results of the scale curve method. The results indicate the acceptable performance of data mining models compared to the scale curve. The results also showed that the GEP model with R2=0.98, RMSE=0.74 and MBE=0.00047 has better performance than the SVM, ANFIS, MARS and GMDH models. The SRC method had the lowest R square value 0.61 and average RMSE 75 and MBE 20. In general, all five data mining methods showed better performance than the SRC sediment ranking curve.

Abstract 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. 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 Sensivity 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, the responses of peak discharge and runoff volume to these parameters were examined across seven rainfall events characterized by different intensities and durations. 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.