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Due to the scarcity of water supplies, coastal groundwater quality most importantly influences sustainable development in the coastal region. Rising groundwater pollution through heavy metal contamination is an intense health hazard and environmental concern worldwide. This study shows that 27%, 32%, and 10% of the total area come under the categories very high, high, and very low human health hazard index (HHHI) accordingly. This area's water quality is also much polluted; the study shows approximately 1% has very good water quality. High concentrations of Fe, As, TDS, Mg 2+ , Na, and Cl - are relatively noticed in the portion of the western part of this district. The concentration of heavy metals in coastal aquifers influences the groundwater pollution of that region. The average heavy metal concentration in this region is 0.20 mg/l (As) and 1.160 mg/l (TDS). The groundwater quality and hydrogeochemical properties are determined through the Piper diagram. The study stated that TDS, Cl - (mg/l), and Na + (mg/l) are the most regulatory issues of vulnerability. In the present study region, a huge number of alkaline substances are present resulting in the water being unfit for drinking purposes. Lastly, it is clear from the study's findings that multiple risks exist there like As, TDS, Cl - , and other hydrochemical parameters in the groundwater. The proposed approach applied in this research work may be a pivotal tool for predicting groundwater vulnerability in other regions., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl - , the value of which is 0.71. Other factors like-As, F - , EC and Mg 2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies., (© 2024. The Author(s).)

Determining the degree of high groundwater arsenic (As) and fluoride (F - ) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F - concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F - contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination., (© 2024. The Author(s).)

Groundwater is the most reliable source of freshwater for human well-being. Significant toxic contamination in groundwater, particularly in the aquifers of the Ganges delta, has been a substantial source of arsenic (As). The Sundarban Biosphere Reserve (SBR), located in the southwestern part of the world's largest Ganges delta, suffers from As contamination in groundwater. Therefore, assessment of groundwater vulnerability is essential to ensure the safety of groundwater quality in SBR. Three data-driven algorithms, i.e. "logistic regression (LR)", "random forest (RF)", and "boosted regression tree (BRT)", were used to assess groundwater vulnerability. Groundwater quality and hydrogeochemical characteristics were evaluated by Piper, United States Salinity Laboratory (USSL), and Wilcox's diagram. The result of this study indicates that among the applied models, BRT (AUC = 0.899) is the best-fit model, followed by RF (AUC = 0.882) and LR (AUC = 0.801) to assess groundwater vulnerability. In addition, the result also indicates that the general quality of the groundwater in this area is not very good for drinking purposes. The applied methods of this study can be used to evaluate the groundwater vulnerability of the other aquifer systems., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

Groundwater (GW) is a precious resource for human beings as we depend on it as a source of fresh drinking water, agricultural practices, industrial and domestic uses, etc. Extreme exposure of arsenic (As) and fluoride (F-) concentrations along the coastal GW aquifers of "South 24 Parganas and East Medinipur" diluted the quality of GW and created serious health issues. Various chronic health disorders such as - black foot disease, fluorosis skin cancer, cardiac problems, and other water borne diseases have been noticed in these two coastal districts. The comprehensive entropy-weighted water quality index (EWQI) and health risk assessment (HRA) were applied to evaluate the quality of GW and probable health risks in the coastal districts. Monte Carlo simulation and sensitivity analysis methods were simultaneously adopted to identify the non-carcinogenic health risk assessment due to regular ingestion of contaminated GW. As the study region is densely populated and part of the Sundarbans Ramsar site, it has greater importance at the international level along with regional importance to address the GWQ of this region. The major findings of the present study highlight that almost 55% of the study area is confronting serious GW quality issues and associated probable health risk (HR) due to the intense accumulation of As and F- in the GW aquifers of the study area. Children's health is more vulnerable due to the consumption of As containing GW, and adults are highly affected due to the intake of F- bearing GW in the coastal districts. The findings of the current study will draw the attention of hydrologists, groundwater management authorities, government bodies, and NGOs to regulate and monitor the GW aquifers routinely, enhance GW quality, minimizing the health hazards and sustainable water management in a more scientific and sustainable way which must be advantageous for coastal people., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

An attempt has been adopted to predict the As and NO 3 concentration in groundwater (GW) in fast-growing coastal Ramsar region in eastern India. This study is focused to evaluate the As and NO - concentration in groundwater (GW) in fast-growing coastal Ramsar region in eastern India. This study is focused to evaluate the As and NO 3 - vulnerable areas of coastal belts of the Indo-Bangladesh Ramsar site a hydro-geostrategic region of the world by using advanced ensemble ML techniques including NB-RF, NB-SVM and NB-Bagging. A total of 199 samples were collected from the entire study area for utilizing the 12 GWQ conditioning factors. The predicted results are certified that NB-Bagging the most suitable and preferable model in this current research. The vulnerability of As and NO 3 - concentration shows that most of the areas are highly vulnerable to As and low to moderately vulnerable to NO 3 . The reliable findings of this present study will help the management authorities and policymakers in taking preventive measures in reducing the vulnerability of water resources and corresponding health risks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The vulnerability of groundwater in the coastal regions in terms of As, F - , and NO 3 exposure is growing rapidly. Hence, the present study focused on assessing groundwater quality, ecological richness, and HR in the coastal districts of West Bengal by applying field-based CD, GWQI, ERI, and HRI techniques. After assessing the GW vulnerability, it is stated that approximately 40-50 % area of the two selected coastal district's GW is poor to very poor in quality, the ecology of GW is threatened, and human health is faced serious risk for both dry and wet season. The Wilcox and USSL diagram verified that nearly 50 % GW aquifers of coastal district of West Bengal are not fit for irrigation and drinking. The findings of this study will be beneficial to manage and control groundwater vulnerability in the coastal regions for water scientists, policy makers, and researchers as well in sustainable way.- exposure is growing rapidly. Hence, the present study focused on assessing groundwater quality, ecological richness, and HR in the coastal districts of West Bengal by applying field-based CD, GWQI, ERI, and HRI techniques. After assessing the GW vulnerability, it is stated that approximately 40-50 % area of the two selected coastal district's GW is poor to very poor in quality, the ecology of GW is threatened, and human health is faced serious risk for both dry and wet season. The Wilcox and USSL diagram verified that nearly 50 % GW aquifers of coastal district of West Bengal are not fit for irrigation and drinking. The findings of this study will be beneficial to manage and control groundwater vulnerability in the coastal regions for water scientists, policy makers, and researchers as well in sustainable way., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

One of the fundamental sustainable development goals has been recognized as having access to clean water for drinking purposes. In the Anthropocene era, rapid urbanization put further stress on water resources, and associated groundwater contamination expanded into a significant global environmental issue. Natural arsenic and related water pollution have already caused a burden issue on groundwater vulnerability and corresponding health hazard in and around the Ganges delta. A field based hydrogeochemical analysis has been carried out in the elevated arsenic prone areas of moribund Ganges delta, West Bengal, a part of western Ganga- Brahmaputra delta (GBD). New data driven heuristic algorithms are rarely used in groundwater vulnerability studies, specifically not yet used in the elevated arsenic prone areas of Ganges delta, India. Therefore, in the current study, emphasis has been given on integration of heuristic algorithms and random forest (RF) i.e., "RF-particle swarm optimization (PSO)", "RF-grey wolf optimizer (GWO)" and "RF-grasshopper optimization algorithm (GOA)", to identify groundwater vulnerable zones on the basis of field based hydrogeochemical parameters. In addition, correspondence health hazard of this area was assessed through human health hazard index. The spatial distribution of groundwater vulnerability revealed that middle-eastern and north-western part of the study area covered by very high and high, whereas central, western and south-western part are covered by very low and low vulnerability zones in outcomes of all the applied models. The evaluation result indicates that RF-GOA (AUC = 0.911) model performed the best considering testing dataset, and thereafter RF-GWO, RF-PSO and RF with AUC value is 0.901, 0.892 and 0.812 respectively. Findings also revealed the groundwater in this study region is quite unfavorable for drinking and irrigation purposes. The suggested models demonstrate their usefulness in foretelling sustainable groundwater resource management in various deltaic regions of the world through taking appropriate measures by policy-makers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The problem of drought in India is a major issue in terms of various adverse impacts on livelihood of society. Drought Early Warning System (DEWS), a real-time drought-monitoring tool, has reported that over a fifth of India's geographical area (21.06 %) is suffering drought-like situations. This is 62 % larger than the drought-affected area during the same period last year, which was 7.86 %. Drought affects 21.06 %, with conditions ranging from unusually dry to extremely dry. While 1.63 % and 1.73 % of the area are experiencing 'extreme' or 'exceptional' dry conditions, 2.17 % is experiencing 'severe' dry conditions. Under 'moderate' dry circumstances, up to 8.15 % is possible. In this perspective groundwater vulnerability assessment in the overall country is needed for implementing the sustainable and long-term strategies for escaping from this type of hazardous situation. The main objective of this study is to estimate the drought vulnerability in changing climate which eventually influences the food security of India. The groundwater overdraft is one of the crucial elements in agricultural drought vulnerability. Various related parameters have been selected for estimating the drought vulnerability and its impact to food security in India. Here, MaxEnt (maximum entropy) and ANN (analytical neural network) has been considered in this perspective. The AUC values for the training datasets in the ANN and MaxEnt model are 0.891 and 0.921, respectively. The AUC values in ANN and MaxEnt model for the validation datasets are 0.876 and 0.904, respectively. Here MaxEnt model is most optimal than ANN considering predictive accuracy. From this study analysis it is established that western, south and middle portion of country is very much prone to drought vulnerability. So, special emphases in terms of the regional planning have to be taken into consideration for sustainable planning., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Vulnerability of groundwater is critical for the sustainable development of groundwater resources, especially in freshwater-limited coastal Indo-Gangetic plains. Here, we intend to develop an integrated novel approach for delineating groundwater vulnerability using hydro-chemical analysis and data-mining methods, i.e., Decision Tree (DT) and K-Nearest Neighbor (KNN) via k-fold cross-validation (CV) technique. A total of 110 of groundwater samples were obtained during the dry and wet seasons to generate an inventory map. Four K-fold CV approach was used to delineate the vulnerable region from sixteen vulnerability causal factors. The statistical error metrics i.e., receiver operating characteristic-area under the curve (AUC-ROC) and other advanced metrices were adopted to validate model outcomes. The results demonstrated the excellent ability of the proposed models to recognize the vulnerability of groundwater zones in the Indo-Gangetic plain. The DT model revealed higher performance (AUC = 0.97) followed by KNN model (AUC = 0.95). The north-central and north-eastern parts are more vulnerable due to high salinity, Nitrate (NO 3 - ) and Arsenic (As) concentrations. Policy-makers and groundwater managers can utilize the proposed integrated novel approach and the outcome of groundwater vulnerability maps to attain sustainable groundwater development and safeguard human-induced activities at the regional level.- ) and Arsenic (As) concentrations. Policy-makers and groundwater managers can utilize the proposed integrated novel approach and the outcome of groundwater vulnerability maps to attain sustainable groundwater development and safeguard human-induced activities at the regional level., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Worldwide, the ever-increasing population and urbanization have put tremendous pressure on groundwater (GW). It has provoked the over-exploitation of groundwater and hindered the percolation of rainwater beneath the earth's surface in another way. The uncertainty of rainfall occurrence and over-exploitation of groundwater created the problem of water scarcity and decreased groundwater height significantly in the hard rock-dominated semi-arid regions. This study is designed to delineate groundwater potential zone (GWPZ) in the hard-rock terrain controlled Bankura district of West Bengal with the help of a knowledge-driven statistical technique, analytical hierarchy process (AHP), in remote sensing and GIS environment. The GWPZ map has been prepared by compiling nine groundwater controlling parameters: geology, geomorphology, slope, land use–land cover (LULC), rainfall, drainage density, soil texture, topographical wetness index (TWI) and lineament density. Among all the parameters, rainfall, geology, slope and LULC have played a dominant role in groundwater availability in the Bankura district, and the weightage of these parameters is 0.28, 0.20, 0.14 and 0.11, respectively. These four layers have facilitated to accumulation of groundwater potentiality in the Bankura district and surroundings. Our study of the GWPZ map revealed that 13.36, 33.80, 21.05, 19.02 and 12.77% of the area of the Bankura district is under very good, good, moderate, poor and very poor condition, respectively, for targeting GW in the future. Very high to high groundwater yielding spots are observed in valley fills, flood plains, vegetation-covered areas, agricultural land and low-lying areas of the study area. The result is evaluated by employing the receiver operation characteristics (ROC) and area under curve (AUC) technique using the well's yield groundwater data, and this value (0.757) also displayed the reliability of this work. However, the GWPZ map of the hard rock-dominated Bankura district has been delineated successfully; the findings of this study can be useful to the planners, engineers and policymakers for proper utilization, evaluation and management of groundwater in this water-scarce semi-arid region to ensure its sustainable development. [ABSTRACT FROM AUTHOR]

Land-subsidence (LS) is a common geo-hazard in many regions of the world. The overexploitation of subsurface and ground water, followed by collapse of underground cavities, is one of the mechanisms that cause LS. Optimal prediction of LS is difficult due to limitations in monitoring, surveying and information about the mechanism and process of LS. Therefore, LS susceptibility mapping (LSSM) is crucial to prevent and reduce the economic damage of natural and manmade catastrophes. This study in the Isfahan Province, Iran, aimed to map LS susceptibility using a hybrid model of adaptive neuro-fuzzy inference system–genetic algorithm (ANFIS–GA) and three stand-alone machine-learning (ML) models: support vector machine, maximum entropy and ANFIS. In total, 155 LS historical points were collected, and 12 LS conditioning factors (LSCFs) were compiled for each site. These factors were elevation, slope, aspect, topographic wetness index, plan curvature, distance to stream, drainage density, groundwater withdrawal, lithology, distance to fault, normalized difference vegetation index and land use and land cover. These data were input into the models in five training-to-validation sample ratios, designated D1 (90:10), D2 (80:20), D3 (70:30), D4 (60:40) and D5 (50:50). The importance of these variables to the occurrence of LS was calculated using a random forest algorithm. Algorithm performance was evaluated through receiver operating characteristicarea under the curve analysis and the results show ANFIS–GA to be the optimal model, with average training and validation AUC values of 0.9644 and 0.9416, respectively. The results demonstrate that LSSMs can be effectively prepared to support decision makers in land use planning and allocation through the development of suitable management strategies. [ABSTRACT FROM AUTHOR]

Delineation of the groundwater's potential zones is a growing phenomenon worldwide due to the high demand for fresh groundwater. Therefore, the identification of potential groundwater zones is an important tool for groundwater occurrence, protection, and management purposes. More specifically, in arid and semi-arid regions, groundwater is one of the most important natural resources as it supplies water during the drought period. The present research study focused on the delineation of potential groundwater zones in Saveh City, the northern part of the Markazi Province in Iran. The groundwater potential mapping was prepared using hybrid deep learning and machine learning algorithm of the boosted tree (BT), artificial neural network (ANN), deep learning neural network (DLNN), deep learning tree (DLT), and deep boosting (DB). This study was carried out by using fourteen groundwater potential conditioning factors and 349 each for springs and non-springs points. The performance of each model was validated through statistical analysis of sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and receiver operating characteristic (ROC)-area under curve (AUC) analysis. The validation result showed that the success rate of AUC is very good for the DB model (0.87–0.99) and other models are also good i.e. BT (0.81–0.90), ANN (0.77–0.82), DLNN (0.84–0.86), and DLT (0.83–0.91). Among the several factors used in this study altitude, rainfall, distance to fault and soil types are the more important conditioning factors for groundwater potential modeling. Finally, all the models in this study had high efficiency in groundwater potential mapping, but it is recommended to use the Deep Boost model due to the better results in future studies. The result of this work will be useful to planners for optimal use and future planning of groundwater. [ABSTRACT FROM AUTHOR]

[Display omitted] • Huge population pressure and heavy metal concentration increase health risk in SBR. • GWQI and ERI both are very high in the coastal blocks of SBR. • South western part of SBR represents very high HHHI. • Very high concentrations of Cl, EC, As, NO 3 , F in groundwater are major challenges. The concept of groundwater (GW) vulnerability emerged as a response to the escalating contamination of GW globally. Over recent decades, both scholars and policymakers have extensively utilized GW vulnerability assessments to safeguard groundwater from pollution. Within the Sundarban Biosphere Reserve (SBR), inhabitants residing near coastal aquifers face significant challenges, notably the scarcity of fresh water during the hot and dry season. Furthermore, there is a concern regarding the presence of heavy metals (HMs) in this region, adversely impacting GW quality. In both Indian and global contexts, HM contamination, including arsenic (As), fluoride (F‾), and nitrate (NO 3 –), and its repercussions on human health (HH) in coastal districts, represent critical issues. In light of these challenges, this study aims to assess GW vulnerability and its implications for HH in SBR utilizing the Hazard Quotient (HQ) approach. Fifteen hydro-geochemical parameters have been selected for this research endeavor. Various statistical indices, such as "groundwater quality index (GWQI)", "degree of contamination (CD)", "index of ecological risk (ERI)", and "human health hazard index (HHHI)", have been employed to accomplish the study objectives. Piper's and Box plot diagrams were utilized to assess hydrogeochemical properties and GW quality. The findings reveal average concentrations of HMs, such as As, NO3–, and F‾ in the study area, to be 0.204 mg/l, 6.28 mg/l, and 0.79 mg/l, respectively. Among all variables, a positive correlation of 0.876 is observed between electrical conductivity (EC) and sodium ions (Na+). Eventually, the outcomes of this study can provide valuable insights for planners and policymakers to make informed decisions regarding the sustainable management of water resources. [ABSTRACT FROM AUTHOR]

The deficiency of freshwater has become a global issue in the recent era, especially in water-scarce hard rock region including India. Groundwater (GW) as a natural resource is decreasing at an alarming rate in West Bengal, India. Sustainable use and planning for better management of groundwater resources are essential; thus, spatial modelling of GW distribution requires proper assessment to conserve and manage the groundwater resource. Machine learning algorithms in RS-GIS environment plays a crucial role in exploration, assessing, monitoring and conserving groundwater resource in this regard. Logistic regression (LR), support vector machine (SVM) and random forest (RF) were used to develop groundwater potential zone (GWPZ) of water-stressed district Purulia with the help of 10 GW controlling factors including geology, geomorphology, lineament density, slope, soil texture, drainage density, GW level, rainfall, NDVI and NDWI. Multi-collinearity analysis was also used to eliminate collinearity issues among all controlling factors. In this study, the total area has been divided into five groups under the very low, low, moderate, high and very high groundwater potential zone categories. It has been calculated that most of the area has moderate groundwater potentiality, which is 29% of the total regions. About 8%, 19%, 25% and 18% of the study area fall under the very low, low, high and very high zones. Finally, all the adopted models were validated through ROC–AUC using GW depth data from CGGB and 484 validating point datasets in this area. The AUC values of adopted LR, SVM and RF models are 0.801, 0.849 and 0.878, respectively; implies that RF is a more reliable algorithm with better predictive ability than other models in the study area. This study's findings will help decision-makers take proper strategies and planning of groundwater resource management for this particular water-scare hard rock region. [ABSTRACT FROM AUTHOR]

Groundwater is the most important source of water among the various water form on the earth's surface. It supports life on the earth and is therefore treated as more significant than surface water. The groundwater of the Ganges delta aquifers has suffered immensely in the last few decades due to various anthropogenic causes apart from natural causes. The high density of population, urbanization, industrialization, and deforestation along the Ganges delta region have been forced to increase the As and F− concentration as human-induced causes apart from natural processes such as high infiltration rate, excessive sedimentation, seawater intrusion, geological formation, etc. The concentration of As and F− in the groundwater aquifers of the Ganges delta increases the vulnerability to various health issues. Extreme exposure to various heavy metals in the groundwater aquifers caused to affect negatively on human health and created different life-losing chronic diseases. Therefore, the present study focused on the assessment of GWQ, evaluation of ecological status, and estimation of probable health risks in the Ganges delta region due to high exposure of As and F− into the groundwater aquifer. The field-based statistical techniques such as degree of contamination (CD), groundwater quality index (GWQI), ecological richness index (ERI), and health risk index (HRI) have been adopted for assessing the As and F− vulnerability including multi-collinearity and correlation matrix. The result illustrates that nearly 50–55% and 60–70% of the Ganges delta region is immensely suffering from poor groundwater quality, ecological diversity, and various health issues in dry and wet seasons respectively in terms of As and F− vulnerability. The USSL and Wilcox diagram confirmed that more than 50% of groundwater aquifers are not suitable for drinking and irrigation. The applied techniques and the outcomes of the current research can be beneficial to the researchers, management authorities, water scientists, and decision-makers in managing the vulnerability of As and F− exposure by adopting sustainable strategies and policies. [Display omitted] • The variability of GW contamination noticed in the Gangetic delta. • About 50–55% and 60–70% area Gangetic delta's GW not suitable for drinking. • Extreme As and F− contamination has created serious health issues. • Regular monitoring and sustainable policy are recommended. [ABSTRACT FROM AUTHOR]

The suitability of groundwater (GW) and the corresponding health risk caused by heavy metals become prime concerns in the recent era; so, the determination of GW quality most important for drinking, agricultural, and domestic purpose is the foremost responsibility of the concurrent researchers. Our study intends to develop an innovative approach to delineate the health hazard risk zone (HHRZ) association to the hydro chemical condition of available GW of the most prominent Indo-Bangladesh Ramsar region by coupling the groundwater quality measurement (GWQM), degree of concentration (Cd), and health risk index (HRI) with the help of hazard quotient (HQ) method. A total of 199 GW samples were collected from the Ramsar sites of both countries and twelve (12) significant physiochemical properties, namely Sodium (Na+), Potassium (K+), Magnesium (Mg2+), Nitrate (NO 3 −), Bicarbonate (HCO 3 -), Phosphate (PO 4 2−), Fluoride (F−), Chloride (Cl−), Sulphate (SO 4 2−), pH, Arsenic (As) and Electoral conductivity (EC) considered as water quality and health hazard causative factors for this research work. This study also assesses the individual health of each parameter and anthropogenic pressure on this Ramsar region. The produced GWQM reveals that the GW of extensive areas in the eastern, north-east, central, and south-central zone is characterized by unfit for consumption (11.47%), very poor (14.27%), and poor (20.76%) categories; whereas, health hazard map also identified quite a similar zone for significant health risk and wide geographical area of Indo-Bangladesh Ramsar site fall under very high (17.49%), high (13.26%), moderate (7.91%) health risk zone. Therefore, our study will help the policymakers, and governments of both countries in ensuring the monitoring plans, strict water management policies, and adoption of health hazard mitigating measures to ensure the well beings of the exposed inhabitants of this coastal Ramsar site region. [Display omitted] • GW suitability and related health risk were assessed by GWQM, Cd, HQ and HRI novel methods. • Eastern, northeast, central, and south-central parts are most hazardous in GWQM and HHRZ. • The anthropogenic pressure is significant in the Indo-Bangladesh Ramsar region. • Wilcox, Piper, USSL diagram reveals the GW suitability for irrigation purposes. [ABSTRACT FROM AUTHOR]