Employing machine learning to predict the occurrence and spatial variability of high fluoride groundwater in intensively irrigated areas

Xiangcao, Zhou; Su, Chunli; Xianjun, Xie; Ge, Weili; Xiao, Ziyi; Yang, Liangping; Pan, Hongjie

doi:10.1016/j.apgeochem.2024.106000

Applied Geochemistry

2024

DOI: 10.1016/j.apgeochem.2024.106000

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Employing machine learning to predict the occurrence and spatial variability of high fluoride groundwater in intensively irrigated areas

Zhou Xiangcao,

Chunli Su,

Xie Xianjun

et al.

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Cited by 2 publications

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Quantitative Assessment and Validation of Groundwater Pollution Risk in Southwest Karst Area

Liu,

Huan,

et al. 2024

Expo Health

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Groundwater pollution risk assessment is a useful tool for groundwater pollution prevention and control. However, it is difficult to accurately quantify groundwater flow and contaminant fluxes in karst areas and different types of karst areas have different hydrogeological characteristics. Therefore, the assessment of groundwater pollution risk in karst areas must use different assessment indicator systems. This study developed a new methodology that modified the vulnerability assessment model PLEIK, determined pollutant fluxes considering hydrogeological conditions, and revised parameter weights using the random forest method. The resulting PLEIKD-RF model was used to assess the risk of groundwater contamination in the southwestern karst region and its validity was verified. The results showed that the groundwater pollution risk in the region was low, with 65.64% of the low and relatively low risk areas located in the middle and high mountainous regions. 11.81% of the high and relatively high risk areas were sporadically located in the western and central regions, which were mainly controlled by the distribution of the pollution sources and the karst development. The accuracy of the results of groundwater pollution risk assessment in the study area was 71.87% as verified by the horizontal difference method. The results of the sensitivity analysis indicated that accurate, detailed, and representative data on the protective layer, surface water-groundwater interactions, and pollution source loads would improve the accuracy of groundwater pollution risk zoning. This assessment method provided a reference for similar assessments and the results provide a basis for the protection and management of groundwater resources in the region.

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Quantitative Assessment and Validation of Groundwater Pollution Risk in Southwest Karst Area

Liu,

Huan,

et al. 2024

Expo Health

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Prediction of geogenic source of groundwater fluoride contamination in Indian states: A comparative study of different supervised machine learning algorithms

Singh,

Mehta

2024

Journal of Water and Health

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India has been dealing with fluoride contamination of groundwater for the past few decades. Long-term exposure of fluoride can cause skeletal and dental fluorosis. Therefore, an in-depth exploration of fluoride concentrations in different parts of India is desirable. This work employs machine learning algorithms to analyze the fluoride concentrations in five major affected Indian states (Andhra Pradesh, Rajasthan, Tamil Nadu, Telangana and West Bengal). A correlation matrix was used to identify appropriate predictor variables for fluoride prediction. The various algorithms used for predictions included K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector classifier (SVC), Gaussian NB, MLP classifier, decision tree classifier, gradient boosting classifier, voting classifier soft and voting classifier hard. The performance of these models is assessed over accuracy, precision, recall and error rate and receiver operating curve. As the dataset was skewed, the performance of models was evaluated before and after resampling. Analysis of results indicates that the RF model is the best model for predicting fluoride contamination in groundwater in Indian states.

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Employing machine learning to predict the occurrence and spatial variability of high fluoride groundwater in intensively irrigated areas

Cited by 2 publications

References 44 publications

Quantitative Assessment and Validation of Groundwater Pollution Risk in Southwest Karst Area

Quantitative Assessment and Validation of Groundwater Pollution Risk in Southwest Karst Area

Prediction of geogenic source of groundwater fluoride contamination in Indian states: A comparative study of different supervised machine learning algorithms

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