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Application of Artificial Intelligence in Predicting Groundwater Contaminants

Abstract

This study presents a critical review of the application of artificial intelligence (AI) in developing prediction models of globally concerning groundwater contaminants, including arsenic, fluoride, and nitrate. Groundwater is arsenic-contaminated in 109 countries, fluoride-contaminated in 84, and nitrate-polluted in 60. Cumulatively, these groundwater contaminations adversely impact the lives of more than 300 million of these countries’ inhabitants. Twenty-four countries have problems with all three contaminants. Fifty-nine countries have issues with arsenic and fluoride in their groundwater, and 63 have at least two groundwater-contamination problems. An array of AI techniques, including machine learning (ML) and deep learning (DL), has been applied in developing arsenic-, fluoride-, and nitrate-prediction models, the most frequently used models being logistic regressions and random forests. We recommend developing such prediction models with substantially larger datasets and meaningful, statistically significant predictors. Considering that contamination is highly regional, it is also advisable to develop local prediction models and evaluate at least several robust and relevant algorithms when it comes to final model selection. Hybrid ML–DL models could be especially useful, considering the many-dimensional nature of environmental data. Environmental scientists should also be trained in various AI techniques. AI prediction models can be used as a decision-support system and to create proactive environmental-management policies.

Keywords

  • Groundwater
  • Arsenic
  • Fluoride
  • Nitrate
  • Artificial intelligence
  • Machine learning
  • Deep learning

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Singh, S.K., Shirzadi, A., Pham, B.T. (2021). Application of Artificial Intelligence in Predicting Groundwater Contaminants. In: Singh, A., Agrawal, M., Agrawal, S.B. (eds) Water Pollution and Management Practices. Springer, Singapore. https://doi.org/10.1007/978-981-15-8358-2_4

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