Pressure fluctuations in a hydraulic jump can prove critical in achieving the safe, economical design of stilling basins. This paper describes the application of data-driven methods and experimental measurements to estimate the dimensionless coefficient of pressure fluctuations (\({C}_{p}^{{\prime}}\)) for forced and free hydraulic jumps in a horizontal channel. White-box data-driven methods, including Multivariate Adaptive Regression Splines (MARS), Gene Expression Programming (GEP), Group Method of Data Handling (GMDH), M5 Model Tree (M5MT), Classification and Regression Tree (CART) method, Stronger Variable Creator Machines (SVCM), Evolutionary Polynomial Regression (EPR), and Multi Expression Programming (MEP), were used to estimate \({C}_{p}^{{\prime}}\). The methods were evaluated using statistical indices and graphical plots. The results indicated that white-box models perform well in predicting \({C}_{p}^{{\prime}}\). Two black-box data-driven models were also examined based on an artificial neural network (ANN) and Support Vector Regression (SVR), and both were hybridized with the Particle Swarm Optimization (PSO) algorithm. SHapley Additive exPlanations (SHAP) method and sensitivity analysis were used to assess effective parameters for \({C}_{p}^{{\prime}}\). Although the accuracy of black-box and white-box models in estimating \({C}_{p}^{{\prime}}\) is close. Of all the methods considered, the ANN-PSO and SVR-PSO were the best predictive models, achieving the lowest values of Objective Function (OBJ) of 0.0035 and 0.0022 for forced and free jumps, respectively. Our findings highlight the potential of data-driven methods for solving complex nonlinear problems in environmental fluid mechanics.

In this research, the models of Inflatable weirs with internal pressures of 0.5 and 1.4 at three heights of 0.15, 0.2 and 0.25 m were constructed. The deflectors were used in two geometric sections, including rectangular and triangular shapes, and they were installed at angles of 30, 45, and 60 degrees downstream of the weir body. The results showed that the value of the Cd changes between 0.28 and 0.35 while the range of the ratio of the upstream flow head on the crest to the radius of the downstream side of the inflatable weir varies between 0.2 and 1.5. The flow by crossing over the inflatable weir and colliding with the surface of the downstream stilling basin loses its energy by about 10–85%. The relative horizontal distance of the projectile-jet from the crest varies between 1.6 and 2.8. The effect of the deflector on the Cd is negligible due to being located in the supercritical part of the weir. Examination of the obtained results declared that the deflector has a considerable effect on energy dissipation.

Dust pollution is a major environmental issue that threatens public health and ecosystems. This study investigates the relationship between wetland area reduction and the intensification of dust pollution in southwestern Iran, utilizing satellite data from Landsat, Sentinel-5, and MODIS (2010–2022). High-resolution imagery from Google Earth Engine (GEE) was used to extract dust event frequency data, while Aerosol Optical Depth (AOD) and Absorbing Aerosol Index (AAI) were employed to monitor dust pollution levels. Normalized Difference Water Index (NDWI) analysis revealed that the Hoor Al-Azim and Shadegan wetlands experienced their driest conditions in 2010, 2011, 2021, and 2022. A strong inverse correlation was observed between wetland area and dust event frequency, with correlation coefficients of − 0.85 for Hoor Al-Azim and − 0.90 for Shadegan. When the area of Hoor Al-Azim dropped below 60 km², dust events increased to 20–35 occurrences. Similarly, in Shadegan, dust events exceeded 20 when the wetland area fell below 100 km². Wind rose analysis indicated that prevailing northeast and northwest winds, combined with dry conditions in northern parts of Shadegan, significantly contribute to dust formation. Furthermore, the study established a clear inverse relationship between wetland area reduction and the number of polluted days for both PM10 and PM2.5. For each 1 km² decrease in wetland area, PM10 polluted days increased by 0.03, explaining 56% of the variation, while PM2.5 polluted days increased by 0.05, accounting for 64%. Principal Component Analysis (PCA) further demonstrated that wetland area changes explain 87.5% and 90.1% of the variance in PM10 and PM2.5 levels, respectively. These findings highlight the critical role of wetland preservation in mitigating dust pollution and improving air quality.

In this study, the discharge coefficients (C_d) of SMBF flumes under free and submerged flow conditions were analytically investigated. The dimensionless parameters involved in the discharge coefficient, derived from the dimensional analysis, are the contraction ratio [r_crn = ratio of flume width (w) to channel width (B)], relative head (h_w: the ratio of the upstream head (h) to the $$w$$w ) and, in the case of submerged flow, also the submergence ratio [ $$S_{{\text{r}}} = h_{{\text{t}}} /h_{{\text{u}}}$$S r = h t / h u : downstream flow depth ( $$h_{{\text{t}}}$$h t ) to upstream flow depth (h_u)]. C_d decreases logarithmically from 1.2 to 0.75 in the range of $$h_{w}$$h w between 0.4 and 1.8. The submerged condition does not reduce the $$C_{{\text{d}}}$$C d , but it reduces the discharge capacity (up to 50%), so that in some cases, to pass a given flow discharge, $$h_{{\text{u}}}$$h u should increase by about 100% compared to the free condition.