From fuzzy-TOPSIS to machine learning: A holistic approach to understanding groundwater fluoride contamination

Nandi, Rupsha; Mondal, Sandip; Mandal, Jajati; Bhattacharyya, Pradip

doi:10.1016/j.scitotenv.2023.169323

Cited by 6 publications

(3 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The single factor pollution index has been done to evaluate the groundwater contamination with a special focus on F – , by the following equation I i = C i C o where I i represents the specific indices, C i signifies the toxic component concentration in groundwater and C o denotes the permissible concentration of the toxic contaminant set by the standard guidelines. The respective C o value for F – in drinking water is 1.5 mg/L. , Groundwater contamination factors are classified into 4 groups or classes: first group/class: I i < 1.0 (low contamination), second group/class: 1.0 ≤ I i < 3.0 (moderate contamination), third group/class: 3.0 ≤ I i < 6.0 (considerable contamination) and fourth group/class: I i ≥ 6.0 (high contamination). , …”

Section: Methodsmentioning

confidence: 99%

“…Fluoride concentration in groundwater was estimated electrochemically with the help of an ion-selective electrode (ISE) meter (Model: Orion Star A214, Thermo Scientific) combined with F – ion selective electrode probe (Orion ISE model no 9609BNWP) following the recommended protocol by ASTM (American Society for Testing and Materials). , Aliquots of 0.1, 1, and 10 mg/L were directly prepared through serial dilution from the standard stock solution of F – (100 mg/L) (Orion 940907, Thermo Scientific). The calibration was performed using the same combination solution containing Total Ionic Strength Adjusting Buffer III (TISAB-III) (Orion 940911, Thermo Scientific) in a ratio of 1:10 (buffer:sample) .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Investigation of Groundwater Fluoride Dynamics in the Lower Gangetic Plain Including Natural Influences with Economic and Health Issues

De,

Das,

Joardar

et al. 2024

ACS EST Water

View full text Add to dashboard Cite

The lower Gangetic Plain in West Bengal, India, has previously been reported to have groundwater fluoride (F − ) contamination and its hazard; however, more information was needed about the precise location of contamination. The current study highlighted these knowledge gaps covering the toxicity of F − through groundwater by emphasizing the role of hydrogeochemical attributions in F − mobilization, people's socioeconomic behavior, and assessing the possible health risks of the local population. Approximately, 10% of groundwater samples exceeded the recommended value of F − with a moderate contamination factor. The groundwater is highly saline (Na−Cl type) in nature. The intermediate zone between the area of 'rock dominance' and 'precipitation dominance' controlled the leaching of F − from host silicate minerals to groundwater. The saturation index established geogenic activity as the responsible factor and found silicate weathering mineral muscovite for F − leaching in groundwater. The average utility cost for 20 L of safe drinking water was US$ 7.58 per month from affordable households. Total hazard quotient (THQ) was observed maximum for infants and significant ingestion rate (IR), body weight (BW), and body surface area (SA) were the responsible factors for the studied age groups. Fluoride-safe drinking water supply strategies are a necessary step in the studied area.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Investigation of Groundwater Fluoride Dynamics in the Lower Gangetic Plain Including Natural Influences with Economic and Health Issues

De,

Das,

Joardar

et al. 2024

ACS EST Water

View full text Add to dashboard Cite

show abstract

“…Junlin Cao and Fangfang Xu [22] combined the fuzzy TOPSIS method based on fuzzy entropy weight (VEWFTOPSIS), the TOPSIS method based on entropy and interval linguistic intuitionistic fuzzy sets (EILIF-TOPSIS), and the intuitionistic fuzzy TOPSIS method based on information entropy attribute importance (IEAI-IF-TOPSIS), proposing a comprehensive MADM method to evaluate large-scale projects. Fahmi, R. Nandi, and Ali et al [23][24][25] further improved the TOPSIS method by proposing the Fuzzy TOPSIS (F-TOPSIS) method, and applied this method to rank major projects in various fields, including agriculture and electrical engineering. Watrobski, J [26] addressed a limitation in classic MCDA, where models are based solely on a single set of input data.…”

Section: Introductionmentioning

confidence: 99%

Benefit Evaluation of Carbon Reduction in Power Transmission and Transformation Projects Based on the Modified TOPSIS-RSR Method

Wang,

Chen,

Zhao

et al. 2024

Energies

View full text Add to dashboard Cite

In order to fully achieve energy saving goals, it is necessary to establish a comprehensive evaluation system for carbon reduction in transmission and transformation projects. Subsequently, weights were assigned to these indicators using a combination of the fuzzy analytical hierarchy process (FAHP) and the entropy weight method (EWM) through both subjective and objective methods. Finally, the ultimate weights were obtained by applying the principle of minimum information. During the construction of the evaluation model, the rank–sum ratio (RSR) method was introduced into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for approximating ideal solution ranking. And the Euclidean distance in TOPSIS was replaced with standardized Euclidean distance, effectively avoiding evaluation discrepancies caused by different dimensions. The modified TOPSIS-RSR method was utilized to evaluate and rank power transmission and transformation projects in four regions. By comparing the test values of the two models, the superiority of the enhanced model was confirmed. Furthermore, the GM (1,1) model is used to predict the electricity sales volume of the optimal ranking area. This evaluation model can also be applied to the benefit evaluation of carbon reduction benefits in power transmission and transformation projects in other regions.

show abstract

Deciphering geochemical fingerprints and health implications of groundwater fluoride contamination in mica mining regions using machine learning tactics

Nandi,

Mondal,

Mandal

et al. 2024

Environ Geochem Health

View full text Add to dashboard Cite

From fuzzy-TOPSIS to machine learning: A holistic approach to understanding groundwater fluoride contamination

Cited by 6 publications

References 49 publications

Investigation of Groundwater Fluoride Dynamics in the Lower Gangetic Plain Including Natural Influences with Economic and Health Issues

Investigation of Groundwater Fluoride Dynamics in the Lower Gangetic Plain Including Natural Influences with Economic and Health Issues

Benefit Evaluation of Carbon Reduction in Power Transmission and Transformation Projects Based on the Modified TOPSIS-RSR Method

Deciphering geochemical fingerprints and health implications of groundwater fluoride contamination in mica mining regions using machine learning tactics

Contact Info

Product

Resources

About