Water Science and Technology, Environmental Science, Environmental Science, Pollution
9
Scopus Publications
92
Scholar Citations
7
Scholar h-index
4
Scholar i10-index
Scopus Publications
Water quality index prediction via a robust machine learning model using oxygen-related indices for river water quality monitoring Amin Arzhangi, Sadegh Partani Scientific Reports, 2026 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 (WQIOIs) by integrating key oxygen-related parameters, including Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), and the reaeration coefficients (K1, K2). 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.
Depth-stratified oxygen stress index for quantifying vertical hypoxia and enabling precise management in stratified reservoirs Amin Arzhangi, Sadegh Partani, Ladan Fallah Mehrjerdi Environmental Advances, 2026 This study introduces the Depth-Stratified Oxygen Stress Index (DS_OSI), a depth-explicit metric designed to quantify vertical hypoxia in thermally stratified reservoirs. Hypoxia, which threatens drinking water quality, irrigation supplies, and aquatic ecosystems, is commonly assessed using fixed dissolved oxygen thresholds or complex process-based models that overlook depth-dependent interactions between temperature, ionic content, and stratification intensity. The DS_OSI, developed from vertical profiles of temperature, pH, conductivity, and dissolved oxygen collected at 3–5 meter intervals to a depth of 35 meters in the Sabalan Dam reservoir during peak summer stratification, achieves R² values greater than 0.92. The model offers an unprecedented framework for quantifying oxygen stress, enabling precise estimation of hypoxic volumes and onset depths. It contributes to the sustainable management of water resources by providing a reliable tool for mitigating hypoxia and supporting long-term reservoir health in stratified aquatic systems.
Spatial Analysis of Microbiological Behavior in Sub-Tropical Coastal Wetlands Sadegh Partani, Amin Arzhangi, Ali Jafari, Pone Roshanpour Abbasabadi Transactions on Maritime Science, 2026 This study examines the spatial interactions between microbiological indicators and environmental factors, including water flow characteristics, in the coastal wetlands of Chabahar Bay, Iran, to assess ecological risks and guide conservation strategies. The objectives are to quantify Total Coliforms (TCs) and Fecal Coliforms (FCs) at six sampling stations and identify key environmental drivers of microbial proliferation through qualitative analysis. Field sampling was conducted over three days in October 2018 at six stations along a river feeding Chabahar’s coastal wetland. Water samples were collected during low tide. In-situ measurements of temperature, pH, humidity, and electrical conductivity were recorded. Microbiological analyses quantified total and fecal coliforms using the Most Probable Number (MPN) method. Total Organic Carbon (TOC) was measured using standard laboratory procedures, and Total Organic Matter (TOM) was determined by the loss-on-ignition method. Duplicate samples ensured data reliability, and data normality was tested with the Kolmogorov-Smirnov test. Descriptive statistics, graphical methods, and matrix analysis assessed relationships between TCs, FCs, temperature, pH, EC, TOC, and TOM. TC counts peaked at S2 (1500 MPN/100 ml), exceeding Iranian Class 2 water quality standards (1500 MPN/100 ml), with FCs detected only at S2 (900 MPN/100 ml) and S3 (23 MPN/100 ml). Temperature ranged from 30.6°C (S6) to 35.9°C (S1), showing an inverse relationship with coliform levels. EC ranged from 1900 to 24,610 μS/cm, displaying a strong inverse correlation with TCs and FCs due to salinity-induced osmotic stress. TOC and TOM peaked at S3–S4 (TOC: 2.8–3.2%; TOM: 5.1–6.0%), correlating inversely with TCs and FCs, suggesting microbial competition. pH (7.71–8.06) showed no significant correlation with coliform levels. Matrix analysis confirmed a high correlation between total and fecal coliforms (p < 0.05), with temperature and pH as independent factors. Sewage inputs at S2 drive significant microbial contamination, with coarse sediments and optimal temperatures facilitating coliform proliferation, while high EC and organic matter in mangrove zones (S3–S4) reduce coliform levels through osmotic stress and microbial competition. Mangroves at S4–S5 act as effective biofilters, mitigating contamination. These findings underscore the need for targeted sewage control and mangrove conservation to protect Chabahar Bay’s wetlands from ecological risks. Future research should examine seasonal variations and additional pollutants to improve management strategies.
A multi-scale framework for BOD5 prediction from water quality to hydro-geomorphic interpolation Amin Arzhangi, Sadegh Partani Iscience, 2025 = 0.941, root-mean-squared error [RMSE] = 2.06), significantly outperforming conventional regression models. The findings demonstrate that HyRIM is a powerful tool for generating high-resolution pollution maps and designing more efficient monitoring strategies in complex river systems.
Developing a reliable predictive model for the biodegradability index in industrial complex effluent Sadegh Partani, Amin Arzhangi, Hamidreza Azari, Hamidreza Moheghi Scientific Reports, 2025 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.
Early Warning for Hypoxia Phenomenon Potential in Semiarid River Ecosystems Using Explainable Machine Learning Sadegh Partani, Amin Arzhangi, Farzad Barat Pour, Vilim Filipović Ecosystem Health and Sustainability, 2025 Hypoxia, defined by low dissolved oxygen (DO) levels, is an important ecological challenge in semiarid river ecosystems, which are particularly vulnerable due to agricultural and urban pressures. This study introduces an explainable machine learning framework combined with survival analysis (SA) to predict hypoxia risk in the Karkheh River Basin, Iran. The model integrates SHAP (SHapley Additive exPlanations)-driven interpretability to enhance transparency, allowing stakeholders to identify key environmental predictors. Eleven months of water quality monitoring data collected from 8 stations across the basin were used to train a random forest model, with temperature identified as the dominant predictor for hypoxia onset, with a threshold at 18 °C. Additional key factors such as turbidity, total suspended solids, and nutrient concentrations also significantly influenced DO levels. The model achieved a predictive accuracy of R 2 = 0.84, demonstrating high reliability in forecasting hypoxia risks. The SA component further quantified the timing and duration of hypoxia, revealing that temperature is a crucial factor in hypoxia risk. This early-warning framework is cost-effective and scalable, offering actionable insights for water resource management in regions with limited monitoring infrastructure. The study contributes to Sustainable Development Goals 6 (Clean Water and Sanitation) and 13 (Climate Action) by providing a tool for sustainable management of river ecosystems in data-scarce regions. The interpretability provided by SHAP allows for clear communication of model predictions, facilitating decision-making and stakeholder engagement.
Source apportionment and temporal trend of potentially toxic elements in a sub tropical coastal wetland S Partani, A Jafari, A Arzhangi, A Danandeh Mehr, M Maghrebi Scientific Reports , 2026 2026.0
Spatial Analysis of Microbiological Behavior in Sub-Tropical Coastal Wetlands S Partani, A Arzhangi, A Jafari, PR Abbasabadi Transactions on Maritime Science 15 (1) , 2026 2026.0
Depth-stratified oxygen stress index for quantifying vertical hypoxia and enabling precise management in stratified reservoirs A Arzhangi, S Partani, LF Mehrjerdi Environmental Advances, 100694 , 2026 2026.0
Instant water quality index prediction via reaeration process and hydraulic parameters in the river system A Arzhangi, S Partani, A Danandeh Mehr, F Ezzati, A Saber Communications Sustainability 1 (1), 24 , 2026 2026.0 Citations: 6
Water quality index prediction via a robust machine learning model using oxygen-related indices for river water quality monitoring A Arzhangi, S Partani Scientific Reports , 2026 2026.0 Citations: 5
A multi-scale framework for BOD5 prediction from water quality to hydro-geomorphic interpolation A Arzhangi, S Partani iScience 28 (12) , 2025 2025.0 Citations: 3
Early Warning for Hypoxia Phenomenon Potential in Semi-Arid Rivers Ecosystem Using Explainable Machine Learning ANDVF SADEGH PARTANI, AMIN ARZHANGI, FARZAD BARAT POUR ECOSYSTEM HEALTH AND SUSTAINABILITY 11 , 2025 2025.0 Citations: 11
Developing a reliable predictive model for the biodegradability index in industrial complex effluent S Partani, A Arzhangi, H Azari, H Moheghi Scientific Reports 15 (1), 30108 , 2025 2025.0 Citations: 11
Development of a Biodegradability Index for Urban Rivers Using Detergent Concentration: A Case Study from Tehran, Iran AA Sadegh Partani American Journal of Environmental Science and Engineering 9 (3), 147-156 , 2025 2025.0 Citations: 8
Determining the main driver of hypoxia potential in freshwater inland lakes S Partani, AD Mehr, F Bostanmaneshrad, A Arzhangi, KP Niavol, ... Journal of Cleaner Production 458, 142521 , 2024 2024.0 Citations: 18
ارزیابی ریسک اکولوژیکی فلزات سنگین در رسوبات تالابهای (شوره زار) ساحلی، مطالعه موردی: تالابهای ساحلی خلیج چابهار، اکوسیستم حرا پرتانی, رشیدی, انیسه, جراحی, جعفری, ارژنگی, امین تحقیقات آب و خاک ایران 54 (11), 1733-1757 , 2024 2024.0
Evaluation of the ecological risk of heavy metals in the sediments of coastal wetlandsCase study: coastal wetlands of Chabahar Bay, mangrove ecosystem S Partani, A Rashidi, H Jarahi, A Jafari, A Arzhangi Iranian Journal of Soil and Water Research 54 (11), 1733-1757 , 2024 2024.0 Citations: 7
Identification and evaluation of biological pollution sources of urban runoff S Partani, A Taherian, A Jafari, H Jarahi, A Arzhangi Journal of Environmental Studies 49 (4), 437-456 , 2024 2024.0 Citations: 7
Journal of Environmental Studies S Pertani, A Taherian, A Jafari, H Jarahi, A Arzhangi Journal of Environmental Studies 49 (4) , 2024 2024.0
Archive of SID. ir SR Ehsani, H Soltanifard, H Ghodrati, H Karachi Journal of Environmental Studies 50 (2) , 2024 2024.0
A new spatial estimation model and source apportionment of aliphatic hydrocarbons in coastal surface sediments of the Nayband Bay, Persian Gulf S Partani, AD Mehr, M Maghrebi, R Mokhtari, HP Nachtnebel, ... Science of The Total Environment 904, 166746 , 2023 2023.0 Citations: 16
Counter Claims in Investor-State Treaty-based Arbitration M Ghamami, A Arzhangi Law Quarterly 53 (2), 253-273 , 2023 2023.0
بررسی شاخصهای زیستمحیطی در زیرساختهای سبز در مدیریت رواناب شهری hasan zirabadi sadegh partani, amin arzhangi, ali jafari یازدهمین کنفرانس ملی سامانه های سطوح آبگیر باران , 2022 2022.0
Environmental Advances A Arzhangi, S Partani, LF Mehrjerdi
MOST CITED SCHOLAR PUBLICATIONS
Determining the main driver of hypoxia potential in freshwater inland lakes S Partani, AD Mehr, F Bostanmaneshrad, A Arzhangi, KP Niavol, ... Journal of Cleaner Production 458, 142521 , 2024 2024.0 Citations: 18
A new spatial estimation model and source apportionment of aliphatic hydrocarbons in coastal surface sediments of the Nayband Bay, Persian Gulf S Partani, AD Mehr, M Maghrebi, R Mokhtari, HP Nachtnebel, ... Science of The Total Environment 904, 166746 , 2023 2023.0 Citations: 16
Early Warning for Hypoxia Phenomenon Potential in Semi-Arid Rivers Ecosystem Using Explainable Machine Learning ANDVF SADEGH PARTANI, AMIN ARZHANGI, FARZAD BARAT POUR ECOSYSTEM HEALTH AND SUSTAINABILITY 11 , 2025 2025.0 Citations: 11
Developing a reliable predictive model for the biodegradability index in industrial complex effluent S Partani, A Arzhangi, H Azari, H Moheghi Scientific Reports 15 (1), 30108 , 2025 2025.0 Citations: 11
Development of a Biodegradability Index for Urban Rivers Using Detergent Concentration: A Case Study from Tehran, Iran AA Sadegh Partani American Journal of Environmental Science and Engineering 9 (3), 147-156 , 2025 2025.0 Citations: 8
Evaluation of the ecological risk of heavy metals in the sediments of coastal wetlandsCase study: coastal wetlands of Chabahar Bay, mangrove ecosystem S Partani, A Rashidi, H Jarahi, A Jafari, A Arzhangi Iranian Journal of Soil and Water Research 54 (11), 1733-1757 , 2024 2024.0 Citations: 7
Identification and evaluation of biological pollution sources of urban runoff S Partani, A Taherian, A Jafari, H Jarahi, A Arzhangi Journal of Environmental Studies 49 (4), 437-456 , 2024 2024.0 Citations: 7
Instant water quality index prediction via reaeration process and hydraulic parameters in the river system A Arzhangi, S Partani, A Danandeh Mehr, F Ezzati, A Saber Communications Sustainability 1 (1), 24 , 2026 2026.0 Citations: 6
Water quality index prediction via a robust machine learning model using oxygen-related indices for river water quality monitoring A Arzhangi, S Partani Scientific Reports , 2026 2026.0 Citations: 5
A multi-scale framework for BOD5 prediction from water quality to hydro-geomorphic interpolation A Arzhangi, S Partani iScience 28 (12) , 2025 2025.0 Citations: 3
Source apportionment and temporal trend of potentially toxic elements in a sub tropical coastal wetland S Partani, A Jafari, A Arzhangi, A Danandeh Mehr, M Maghrebi Scientific Reports , 2026 2026.0
Spatial Analysis of Microbiological Behavior in Sub-Tropical Coastal Wetlands S Partani, A Arzhangi, A Jafari, PR Abbasabadi Transactions on Maritime Science 15 (1) , 2026 2026.0
Depth-stratified oxygen stress index for quantifying vertical hypoxia and enabling precise management in stratified reservoirs A Arzhangi, S Partani, LF Mehrjerdi Environmental Advances, 100694 , 2026 2026.0
