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Ecosystems, human health, and long-term sustainability are all badly threatened by polluted groundwater. ML and AI offer promising new approaches to this critical problem. To effectively monitor, anticipate, and manage groundwater contamination, this chapter investigates the use of AI and ML. To identify contamination sources, predict pollution trends, and improve remediation techniques, complex algorithms sift through massive datasets collected from sensors, remote sensing devices, and historical records. Among the main uses discussed are decision-support frameworks for policy-making and resource allocation, predictive modelling for the spread of contaminants, and real-time monitoring of water quality? This chapter delves into how to enhance data collecting and get valuable insights by combining AI-driven solutions with IoT devices. Additionally, we delve into topics like data scarcity, model reliability, and the ethical implications of AI applications. Through analysis of past and future cases, this chapter demonstrates how AI and ML have the ability to revolutionize methods for controlling groundwater pollution and fostering sustainable management of water resources.

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AI and ML-Driven Approaches for Groundwater Pollution Mitigation and Sustainable Management

  • 章节

  • 发表日期：

    2026年

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  • Arivanantham Thangavelu,
  • S. Praveena,
  • Kriti Srivastava,
  • G. Nithya,
  • J. Kartigeyan,
  • B. Sandhiya,
  • V. Bhoopathy

摘要

Ecosystems, human health, and long-term sustainability are all badly threatened by polluted groundwater. ML and AI offer promising new approaches to this critical problem. To effectively monitor, anticipate, and manage groundwater contamination, this chapter investigates the use of AI and ML. To identify contamination sources, predict pollution trends, and improve remediation techniques, complex algorithms sift through massive datasets collected from sensors, remote sensing devices, and historical records. Among the main uses discussed are decision-support frameworks for policy-making and resource allocation, predictive modelling for the spread of contaminants, and real-time monitoring of water quality? This chapter delves into how to enhance data collecting and get valuable insights by combining AI-driven solutions with IoT devices. Additionally, we delve into topics like data scarcity, model reliability, and the ethical implications of AI applications. Through analysis of past and future cases, this chapter demonstrates how AI and ML have the ability to revolutionize methods for controlling groundwater pollution and fostering sustainable management of water resources.