Iran's groundwater hydrochemistry has not been well understood. In this study, Iran's groundwater hydrochemistry is evaluated using a rich, ground‐trusted data sampled from 9,468 wells distributed across the country in 2011. Twelve groundwater quality parameters were analyzed in each sample, resulting in 113,616 parameters over the study period. Examination of anions‐cations shows that concentrations of sodium, calcium, chloride, and sulphate are higher than the acceptable threshold for drinking‐use suggested by the World Health Organization in about 40%, 21%, 25%, and 20% of the samples, respectively. The results of the water quality index reveal that most of the groundwater resources in the central, southern and eastern regions of Iran, which supply the majority of the domestic water for populated cities, do not meet the requirements for drinking‐use. Although the groundwater in northern parts fulfills the requirements for irrigation‐use, it is only suitable for irrigation of salinity‐friendly crops in central, eastern and southern regions. Ionic types and hydrochemistry facies indicate the dominance of mix water type in 13 out of 30 of Iran's sub‐basins, followed by sodium‐chloride water type in nine sub‐basins. Local geology and lithology are mainly attributed to the distribution of groundwater facies in Iran. In general, our findings reveal a distinctive relationship between Iran's geological‐geomorphological features and hydrochemical facies/groundwater quality. The findings can be used in the formulation of new strategies and policies for Iran's groundwater quality management in the future.

Sediment load in fluvial systems is one of the critical factors shaping the river geomorphological and hydraulic characteristics. A detailed understanding of the total sediment load (TSL) is required for the protection of physical, environmental, and ecological functions of rivers. This study develops a robust methodological approach based on multiple linear regression (MLR) and support vector regression (SVR) models modified by principal component analysis (PCA) to predict the TSL in rivers. A database of sediment measurement from large-scale physical modelling tests with 4759 datapoints were used to develop the predictive model. A dimensional analysis was performed based on the literature, and ten dimensionless parameters were identified as the key drivers of the TSL in rivers. These drivers were converted to uncorrelated principal components to feed the MLR and SVR models (PCA-based MLR and PCA-based SVR models) developed within this study. A stepwise PCA-based MLR and a 10-fold PCA-based SVR model with different kernel-type functions were tuned to derive an accurate TSL predictive model. Our findings suggest that the PCA-based SVR model with the kernel-type radial basis function has the best predictive performance in terms of statistical error measures including the root-mean-square error normalized with the standard deviation (RMSE/StD) and the Nash–Sutcliffe coefficient of efficiency (NSE), for the estimation of the TSL in rivers. The PCA-based MLR and PCA-based SVR models, with an overall RMSE/StD of 0.45 and 0.35, respectively, outperform the existing well-established empirical formulae for TSL estimation. The analysis of the results confirms the robustness of the proposed PCA-based SVR model for prediction of the cases with high concentration of sediments (NSE = 0.68), where the existing sediment estimation models usually have poor performance.

The interaction between chemical oxygen demand (COD) and biological oxygen demand (BOD5) in wastewater from Tehran’s Paytakht and Nasirabad Industrial Parks is investigated in this work. Monitoring platforms of industrial parks were the base frame of monthly collection data for laboratory measurements (for BOD5 and COD) and in-situ measurements (for DO, EC and Temperature-T°C) with a frequency of 4-hour samples/day. Backward elimination regression analysis was employed as an integrated procedure to find out effective model removing ineffective independent variables. Multivariate Regression analysis showed a relatively strong linear relationship between COD and BOD, with independent variables with R²=0.64 and R²=0.59, respectively. A prediction model for BOD based on COD was found by analyzing important effluent quality variables using simple linear regression and a strong linear association (BOD = 0.433COD + 222) with R² = 0.94, MSE = 38,829, RMSE = 197.05 was obtained. In all of these regression analyses, model accuracy was assessed by conducting statistical tests on the residuals. To verify and improve the reliability and practicability of model, it is applied of industrial parks’ wastewater records of countries around the world such as Egypt, France, India, Pakistan and Malaysia. The extracted model applied on some of the mentioned countries’ records and the results of BOD prediction was matched by observations in 95% of reliability domain. Variation of BOD-COD ratio was least affected by pH and temperature; the results underline the requirement of localized validation resulting from industry-specific differences and promote cost-effective, quick wastewater evaluation, hence lowering reliance on laboratory-based BOD₅ testing. It defiantly provides the opportunity of analytical and applied researches in south countries toward sustainable industrial wastewater management.

Rivers face increasing pollution, requiring accurate water quality assessment tools. Existing indices like the Water Quality Index (WQI) often overlook the integration of oxygen-related parameters critical to aquatic health. Here, we develop a machine learning model using Support Vector Regression (SVR) to predict the Water Quality Index (WQI _OIs ) by integrating key oxygen-related parameters, including Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), and the reaeration coefficients (K ₁ , K ₂ ). Applied to three rivers in Iran, the model demonstrated high accuracy, with a cross-validated R² > 0.95 and root mean squared error (RMSE) of 0.92 for the Haraz River and 1.41 for the Simineh River. Predictions showed strong correlation ( r = 0.98) with standard indices, and feature importance analysis revealed DO as the most influential parameter. The model’s generalizability was confirmed through validation on independent river datasets, highlighting its robustness across diverse hydrological conditions. This approach offers a scalable, interpretable framework for continuous water quality monitoring, enabling more precise and data-driven management of aquatic ecosystems, particularly in regions with varying environmental factors.