Groundwater quality and modelling, health risks, Artificial intelligence and Machine learning application
23
Scopus Publications
933
Scholar Citations
13
Scholar h-index
13
Scholar i10-index
Scopus Publications
Assessing groundwater quality for drinking purposes using water quality index and machine learning techniques in the Raipur district of central India Prince Kumar, Soumya Sucharita Singha, Sudhakar Singha Environmental Earth Sciences, 2026 Groundwater is an important resource, but contamination-related safety threats and the need for accurate and consistent evaluation are substantial. Therefore, in the current research, groundwater samples (n = 237) were collected during the pre-monsoon season (May–June 2024) from Raipur District, Chhattisgarh, Central India, and evaluated drinking water quality using eleven physico-chemical parameters through a Water Quality Index (WQI), the Entropy Water Quality Index (EWQI), and a newly developed Self-Organizing Map-based Water Quality Index (SWQI). The adopted quality indexing methods were also validated and compared by replicating the computations using a different dataset sourced from the study area. Analysis demonstrated high reliability of SWQI with precision (0.952), rationality (0.985), robustness (0.977), and versatility (0.918); uncertainty of only 03% from error using Monte Carlo simulations, which is high fidelity and supports the trustworthiness of proposed SWQI method for the assessment of groundwater quality. The present study also used four Machine learning (ML) algorithms including Random Forest (RF), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Deep Learning (DL) Machine learning (ML) algorithms to predict the groundwater quality indices. Results showed that DL had the best accuracies, in terms of coefficient of determinations (R² = 0.909, 0.960, and 0.985) while predicting WQI, EWQI, and SWQI. The research illustrates that SOM-based indices are valuable for groundwater quality assessment that can be integrated within ML to provide accurate groundwater quality prediction, which can then inform sound water resource management strategies to offer resource sustainability and water safety.
Groundwater quality assessment in Nalgonda District, Telangana, India: a comprehensive approach using self organizing map Soumya Sucharita Singha, Sudhakar Singha, Prince Kumar Discover Sustainability, 2025 The groundwater geochemistry of an aquifer depends on inter-ionic influences and numerous chemical and physical factors unique to an area. To assess the factors governing the characteristics and quality of groundwater, the present research examined groundwater quality in parts of Telangana State, utilizing 89 groundwater samples collected from the Nalgonda District during March–May 2023. A Total of 12 physicochemical parameters, including pH, TDS, Na + , K + , Ca 2+ , Mg 2+ , HCO 3 − , Cl − , SO 4 2− , NO 3 − , F − and TH, were analyzed, where pH and TDS were measured at sites with portable handheld meters and the rest of the parameters were tested in the laboratory. The analysis detected the presence of many parameters above the standard permissible limits, indicating severe multi-pollutant contamination. An unsupervised soft computing technique, namely self-organizing map (SOM), along with other statistical tools, such as principal component analysis (PCA), saturation index (SI), and Pearson’s correlation approach, was used to identify contamination sources. The SOM recognized three clusters in which elevated groundwater chemical concentrations were related to mineral dissolution and anthropogenic activities. PCA revealed three key principal components, summarizing 85% of the variance for the identification of groundwater pollution sources, further confirming the SOM results. The results indicated that groundwater belonging to SOM-generated Cluster I was more suitable for drinking purposes than Clusters II and III. The overall groundwater quality was assessed using an entropy-based water quality index (EWQI), which showed that a considerable part of the study area had poor (22.27%) or UFD (23.32%) water quality. The EWQI results, along with extensive statistical analysis, reveal the groundwater quality status and probable health risks in the Nalgonda District, which are essential for policymakers and stakeholders to formulate successful water management and remediation strategies.
Sustainable application of edible solute to control reservoir evaporation loss Indramani Dhada, Poonam, Sudhakar Singha, Anoop Kumar Shukla Scientific Reports, 2025 Recently, water preservation globally, particularly in Indian cities, has been prominently featured in newspaper headlines, underscoring its importance. This research explores the innovative use of edible solutes to tackle the challenges of evaporation in reservoirs, including water loss, increased salinity, and ecological disruptions. Traditional methods for controlling evaporation often have environmental drawbacks and high operational costs. By evaluating the environmental impact, cost-effectiveness, and feasibility of edible solutes such as mustard oil, neem oil, til oil, castor oil, cetyl alcohol, and stearyl alcohol, this study investigated their sustainable application in eight reservoirs across Andhra Pradesh and Telangana states in India. Through break-even analysis, the economic viability of edible solutes is compared to that of conventional methods over the lifespan of a reservoir. These findings suggest that edible solutes offer a promising and environmentally friendly alternative, reducing evaporation rates while minimizing the adverse effects on water quality and ecosystems. Despite the initial investment costs, the long-term savings and environmental benefits surpass those of the conventional approaches. This study estimated evaporation rates for eight reservoirs across Andhra Pradesh and Telangana in India (3049 mcm of water/year), showing a significant reduction when cetyl alcohol was used as a solute. Cetyl and stearyl alcohols are highlighted as practical and cost-effective evaporation retardants. Considering the cost of water at one paise per five litres of saved water, the break-even point (BEP) analysis for the adopted scenarios reveals that BEP is achieved for 30%, 10%, and 5% reduction in evaporation within one, two, and three months, respectively. Similarly, for scenario II (one paisa per one litre of saved water), the BEP was achieved at the beginning, 1.5 months, and 2.5 months, considering evaporation reduction by 30%, 10%, and 5%, respectively. Future research should validate the efficacy of microfilms in mitigating evaporation using time-resolved interferometry techniques. This study advocates sustainable water management practices and provides valuable insights for policymakers, water resource managers, and stakeholders seeking efficient solutions for evaporation control in reservoirs.
Indexical methods assessing PTEs distribution in Mahan river command area, central India’s coal mining zone Nirmal Kumar, Mahendra Kumar Tiwari, Rambabu Singh, Sudhakar Singha, Soumya S. Singha, Prasad Babu.K Geochemical Transactions, 2025 The quality of water can significantly affect the regional water resources due to scarcity of potable water in industrial area. The purpose of this study was to explore potentially toxic trace elements (PTEs) contamination and their seasonal variations in different water sources within the coal mining area of the Mahan River command area, Central India. To achieve this, 96 water samples were collected across two distinct seasons and analysed for PTEs. The results indicate that during the pre-monsoon season, the concentrations of Mn (18%), Cu (4%), Pb (8%), Ni (18%), Cd (2%), Al (4%), Cr (2%), and Fe (30%) exceeded permissible limits. In the post-monsoon season, Mn (15%), Pb (6%), Ni (15%), Cd (2%), Al (15%), Fe (46%) and Ba (4%) surpassed the standards. The multiple groundwater pollution indexical methods further revealed that 14% [Heavy metal pollution index (HPI)], 14% [Heavy metal evaluation index (HEI)], 18% [Contamination index (CI)], 14% [the entropy-weight based HM contamination index (EHCI)] and 20% [Heavy metal index (HMI)] of the samples exceeded permissible thresholds during the pre-monsoon season. Similarly, during the post-monsoon period, 10% (HPI), 10% (HEI), 15% (CI), 15% (EHCI) and 17% (HMI) of the samples were above acceptable limits. The relationship between the pH of water and the total load of dissolved metals is established using Caboi plot, confirming that mine water from mine water from Bhatgaon Underground (UG), Mahamaya UG, and Mahan Opencast (OC) [PR40, PR41, PR42, PR43, PR47, and PR48], surrounding rivers, and groundwater sources, exhibited an "Acid-High Metal" characteristic. This suggests significant contamination from acid mine drainage and mineral dissolution. Apart from the anthropogenic inputs, geogenic and environmental processes are responsible for the current distribution of PTEs and their seasonal variations.
Expected health risk out of black carbon and particulate matter in the indoor environment of an industrial cluster of chandigarh in India Indramani Dhada, Sadiq Abdullahi Waziri, Vishal, Sudhakar Singha, Bijaya Kumar Padhi, Shailesh Kumar Samal Scientific Reports, 2025 The global increase in industrialization and its attendant exponential air pollution has posed a significant hazard to the indoor pollution levels of cities and the associated health risks. This study evaluated the health effects of air pollutants discovered inside the bottling industries in Chandigarh cluster in India. PM10, PM2.5, PM1, and black carbon concentrations in the post-monsoon season were monitored, and associated health implications and lung disease were estimated. A positive correlation is established between PM in indoor and outdoor environments. Maximum concentrations for PM10, PM2.5, and PM1 were recorded as 276.8 µg/m3, 97.7 µg/m3 and 66.5 µg/m3 (for indoor) respectively, which are approximately 15 and 6 times higher than their (PM10 and PM2.5) allowable concentrations set by World Health Organization, posing a health threat to the workers and staff of the industries. The lifetime carcinogenic risk of black carbon and the non-carcinogenic risk of particulate matter and black carbon have been assessed using a deterministic and probabilistic model, which shows the marginal difference. The estimated lifetime carcinogenic risk due to black carbon for males and females was observed in the range of 7.20E-05 to 6.17E-05. The spirometry analysis indicates that about 13.04% of the sample population (out of 184 samples) have healthy lungs.
Geospatial delineation of flood susceptible zones using analytical hierarchy process Ravindra Kumar Singh, Soumya Sucharita Singha, Sudhakar Singha, Srinivas Pasupuleti, Vasanta Govind Kumar Villuri Iop Conference Series Earth and Environmental Science, 2023 Demarcation of flood-prone areas based on risk level plays a key role in flood management systems. Geospatial approach has gained much significance among the researcher working in flood risk management. This work incorporated an analytic hierarchy process (AHP) and geographic information system (GIS) to delineate the flood susceptible zones of the lower Damodar River basin. Various influencing factors were initially assigned with weights with respect to their relative importance towards flood susceptibility and integrated into GIS platform by weighted overlay approach. Based on the current study area, the flood zone map was sub-classified into four zones, namely critical, high and low, and safe zone, respectively. Moreover, sensitivity analysis was also performed to evaluate the most significant factors in flood susceptibility using the exclusion of factors from the estimation of flood zone index. Sensitivity analysis showed that River distance and elevation are the most sensitive factors for evaluating flood zonation.
Evaluation of groundwater depletion scenario in balodabazar block, balodabazar, chhattisgarh, India International Journal of Civil Engineering and Technology, 2017
RECENT SCHOLAR PUBLICATIONS
Groundwater quality assessment in Bihar's aquifers: a machine learning approach P Kumar, SS Singha, S Singha Environmental Science and Pollution Research, 1-28 , 2026 2026
Machine Learning-Based Groundwater Quality Assessment of Jharkhand, India P Kumar, SS Singha, B Das, S Singha Water Health and Sustainability: Strategies for Effective Resource … , 2026 2026
Assessing groundwater quality for drinking purposes using water quality index and machine learning techniques in the Raipur district of central India P Kumar, SS Singha, S Singha Environmental Earth Sciences 85 (4), 96 , 2026 2026 Citations: 2
Sustainable application of edible solute to control reservoir evaporation loss AK Dhada, Indramani., Poonam., Singha, Sudhakar., Shukla Scientific Reports 15 (44550) , 2025 2025
Comparative Assessment of Groundwater Quality Using Subjective and Objective Weighting Methods in a Multi-Criteria Decision Analysis Framework SS Singha, S Singha Groundwater for Sustainable Development 31, 101553 , 2025 2025 Citations: 2
Expected health risk out of black carbon and particulate matter in the indoor environment of an industrial cluster of chandigarh in India BKPSKS Indramani Dhada, Sadiq Abdullahi Waziri, Vishal, Sudhakar Singha Scientific Reports 15 (23177) , 2025 2025 Citations: 3
A novel composite machine learning model for the prediction of compressive strength of blended concrete EV Prasad, S Rama Krishna, S Singha Journal of Building Pathology and Rehabilitation 10 (1), 13 , 2025 2025 Citations: 6
Indexical methods assessing PTEs distribution in Mahan river command area, central India’s coal mining zone N Kumar, MK Tiwari, R Singh, S Singha, SS Singha, P Babu. K Geochemical Transactions 26 (1), 2 , 2025 2025 Citations: 1
Groundwater quality assessment in Nalgonda District, Telangana, India: a comprehensive approach using self organizing map SS Singha, S Singha, P Kumar Discover Sustainability 6 (1), 185 , 2025 2025 Citations: 4
Geospatial delineation of flood susceptible zones using analytical hierarchy process RK Singh, SS Singha, S Singha, S Pasupuleti, VG Kumar Villuri IOP Conference Series: Earth and Environmental Science 1280 (1), 012052 , 2023 2023 Citations: 2
Study on predicting compressive strength of concrete using supervised machine learning techniques BV Varma, EV Prasad, S Singha Asian Journal of Civil Engineering 24 (7), 2549-2560 , 2023 2023 Citations: 30
Groundwater characterization and non-carcinogenic and carcinogenic health risk assessment of nitrate exposure in the Mahanadi River Basin of India S Pasupuleti, SS Singha, S Singha, S Kumar, R Singh, I Dhada Journal of Environmental Management 319, 115746 , 2022 2022 Citations: 71
A knowledge-driven multi-criteria decision making-Analytical Hierarchy Process based geospatial modeling for the delineation of fluoride contamination zones in groundwater … S Kumar, S Singha, R Singh, AS Venkatesh, U Gogoi Groundwater for Sustainable Development 18, 100795 , 2022 2022 Citations: 9
Knowledge-driven and machine learning decision tree-based approach for assessment of geospatial variation of groundwater quality around coal mining regions, Korba district … SS Singha, S Singha, S Pasupuleti, AS Venkatesh Environmental Earth Sciences 81 (2), 36 , 2022 2022 Citations: 31
Analytic network process based approach for delineation of groundwater potential zones in Korba district, Central India using remote sensing and GIS SS Singha, S Pasupuleti, S Singha, R Singh, AS Venkatesh Geocarto International 36 (13), 1489-1511 , 2021 2021 Citations: 41
Prediction of groundwater quality using efficient machine learning technique S Singha, S Pasupuleti, SS Singha, R Singh, S Kumar Chemosphere 276, 130265 , 2021 2021 Citations: 417
A fuzzy geospatial approach for delineation of groundwater potential zones in Raipur district, India S Singha, P Das, SS Singha Groundwater for Sustainable Development 12, 100529 , 2021 2021 Citations: 28
Effectiveness of groundwater heavy metal pollution indices studies by deep-learning S Singha, S Pasupuleti, SS Singha, S Kumar Journal of Contaminant Hydrology 235, 103718 , 2020 2020 Citations: 96
Hydrogeochemical modeling based approach for evaluation of groundwater suitability for irrigational use in Korba district, Chhattisgarh, Central India SS Singha, S Pasupuleti SN Applied Sciences 2 (9), 1551 , 2020 2020 Citations: 17
Delineation of groundwater prospect zones in Arang block, Raipur district, Chhattisgarh, Central India, using analytical network process S Singha, S Pasupuleti Journal of the Geological Society of India 95 (6), 609-615 , 2020 2020 Citations: 16
MOST CITED SCHOLAR PUBLICATIONS
Prediction of groundwater quality using efficient machine learning technique S Singha, S Pasupuleti, SS Singha, R Singh, S Kumar Chemosphere 276, 130265 , 2021 2021 Citations: 417
Effectiveness of groundwater heavy metal pollution indices studies by deep-learning S Singha, S Pasupuleti, SS Singha, S Kumar Journal of Contaminant Hydrology 235, 103718 , 2020 2020 Citations: 96
Groundwater characterization and non-carcinogenic and carcinogenic health risk assessment of nitrate exposure in the Mahanadi River Basin of India S Pasupuleti, SS Singha, S Singha, S Kumar, R Singh, I Dhada Journal of Environmental Management 319, 115746 , 2022 2022 Citations: 71
A GIS-based modified DRASTIC approach for geospatial modeling of groundwater vulnerability and pollution risk mapping in Korba district, Central India SS Singha, S Pasupuleti, S Singha, R Singh, AS Venkatesh Environmental Earth Sciences 78 (21), 628 , 2019 2019 Citations: 51
Assessing ground water quality using GIS S Singha, CP Devatha, S Singha, MK Verma International Journal of Engineering Research & Technology 4 (11), 689-694 , 2015 2015 Citations: 46
Analytic network process based approach for delineation of groundwater potential zones in Korba district, Central India using remote sensing and GIS SS Singha, S Pasupuleti, S Singha, R Singh, AS Venkatesh Geocarto International 36 (13), 1489-1511 , 2021 2021 Citations: 41
Knowledge-driven and machine learning decision tree-based approach for assessment of geospatial variation of groundwater quality around coal mining regions, Korba district … SS Singha, S Singha, S Pasupuleti, AS Venkatesh Environmental Earth Sciences 81 (2), 36 , 2022 2022 Citations: 31
Study on predicting compressive strength of concrete using supervised machine learning techniques BV Varma, EV Prasad, S Singha Asian Journal of Civil Engineering 24 (7), 2549-2560 , 2023 2023 Citations: 30
A fuzzy geospatial approach for delineation of groundwater potential zones in Raipur district, India S Singha, P Das, SS Singha Groundwater for Sustainable Development 12, 100529 , 2021 2021 Citations: 28
An analytical hierarchy process-based geospatial modeling for delineation of potential anthropogenic contamination zones of groundwater from Arang block of Raipur district … S Singha, S Pasupuleti, KS Durbha, SS Singha, R Singh, AS Venkatesh Environmental Earth Sciences 78 (24), 694 , 2019 2019 Citations: 26
An integrated approach for evaluation of groundwater quality in Korba district, Chhattisgarh using Geomatic techniques S Singha, S Pasupuleti, VGK Villuri Journal of Environmental Biology 38 (5), 865 , 2017 2017 Citations: 26
Hydrogeochemical modeling based approach for evaluation of groundwater suitability for irrigational use in Korba district, Chhattisgarh, Central India SS Singha, S Pasupuleti SN Applied Sciences 2 (9), 1551 , 2020 2020 Citations: 17
Delineation of groundwater prospect zones in Arang block, Raipur district, Chhattisgarh, Central India, using analytical network process S Singha, S Pasupuleti Journal of the Geological Society of India 95 (6), 609-615 , 2020 2020 Citations: 16
A knowledge-driven multi-criteria decision making-Analytical Hierarchy Process based geospatial modeling for the delineation of fluoride contamination zones in groundwater … S Kumar, S Singha, R Singh, AS Venkatesh, U Gogoi Groundwater for Sustainable Development 18, 100795 , 2022 2022 Citations: 9
Delineation of groundwater potential zones utilising geospatial techniques in Kadiri watershed of Anantapur district, Andhra Pradesh, India S Pasupuleti, DK Sandilya, S Singha, SS Singha, S Saha Journal of Environmental Biology 40 (1), 61-68 , 2019 2019 Citations: 8
A novel composite machine learning model for the prediction of compressive strength of blended concrete EV Prasad, S Rama Krishna, S Singha Journal of Building Pathology and Rehabilitation 10 (1), 13 , 2025 2025 Citations: 6
Groundwater quality assessment in Nalgonda District, Telangana, India: a comprehensive approach using self organizing map SS Singha, S Singha, P Kumar Discover Sustainability 6 (1), 185 , 2025 2025 Citations: 4
Expected health risk out of black carbon and particulate matter in the indoor environment of an industrial cluster of chandigarh in India BKPSKS Indramani Dhada, Sadiq Abdullahi Waziri, Vishal, Sudhakar Singha Scientific Reports 15 (23177) , 2025 2025 Citations: 3
Assessing groundwater quality for drinking purposes using water quality index and machine learning techniques in the Raipur district of central India P Kumar, SS Singha, S Singha Environmental Earth Sciences 85 (4), 96 , 2026 2026 Citations: 2
Comparative Assessment of Groundwater Quality Using Subjective and Objective Weighting Methods in a Multi-Criteria Decision Analysis Framework SS Singha, S Singha Groundwater for Sustainable Development 31, 101553 , 2025 2025 Citations: 2
