首页|Findings from Indian Statistical Institute Provides New Data about Machine Learning (From Fuzzy-topsis To Machine Learning: a Holistic Approach To Understanding Groundwater Fluoride Contamination)
Findings from Indian Statistical Institute Provides New Data about Machine Learning (From Fuzzy-topsis To Machine Learning: a Holistic Approach To Understanding Groundwater Fluoride Contamination)
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NETL
NSTL
Elsevier
Current study results on Machine Learning have been published. According to news reporting out of Jharkhand, India, by NewsRx editors, research stated, “Fluoride (F-) contamination of groundwater is a prevalent environmental issue threatening public health worldwide and in India. This study targets an investigation into spatial distribution and contamination sources of fluoride in Dhanbad, India, to help develop tailored mitigation strategies.” Our news journalists obtained a quote from the research from Indian Statistical Institute, “A triad of Multi Criteria Decision Making (MCDM) models (Fuzzy-TOPSIS), machine learning algorithms {logistic regression (LR), classification and regression tree (CART), Random Forest (RF)}, and classical methods has been undertaken here. Groundwater samples (n = 283) were collected for the purpose. Based on permissible limit (1.5 ppm) of fluoride in drinking water as set by the World Health Organization, samples were categorized as Unsafe (n = 67) and Safe (n = 216) groups. Mean fluoride concentration in Safe (0.63 +/- 0.02 ppm) and Unsafe (3.69 +/- 0.3 ppm) groups differed significantly (t-value = -10.04, p＜0.05). Physicochemical parameters (pH, electrical conductivity, total dissolved solids, total hardness, NO3-, HCO3-, SO42-, Cl-, Ca2+, Mg2+, K+, Na+ and F-) were recorded from samples of each group. The samples from ‘Unsafe group’ showed alkaline pH, the abundance of Na+ and HCO3- ions, prolonged rock water interaction in the aquifer, silicate weathering, carbonate dissolution, lack of Ca2+ and calcite precipitation which together facilitated the F- abundance. Aspatial distribution map of F- contamination was created, pinpointing the ‘contaminated pockets.’ Fuzzy- TOPSIS identified that samples from group Safe were closer to the ideal solution. Among these models, the LR proved superior, achieving the highest AUC score of 95.6 % compared to RF (91.3 %) followed by CART (69.4 %). This study successfully identified the primary contributors to F- contamination in groundwater and the developed models can help predicting fluoride contamination in other areas.”
JharkhandIndiaAsiaAnionsCyborgsEmerging TechnologiesFluoridesHydrofluoric AcidMachine LearningIndian Statistical Institute
NETL
NSTL
Elsevier
