Singh et al. (2026) Application of machine learning/artificial intelligence and IoT in water resources modeling, management, and mitigation

Identification

• Journal: Elsevier eBooks
• Year: 2026
• Date: 2026-01-01
• Authors: Varsha Singh, Dipayon Bachar, R.M. Dinesh Madhushanka Karunarathna, Sadashiv Chaturvedi, Amit Kumar
• DOI: 10.1016/b978-0-443-36394-8.00017-0

Research Groups

• Department of Physics, Faculty of Science, Technology, and Architecture (FOSTA), Manipal University, Jaipur, Rajasthan, India
• Environmental Sciences, Department of Botany, Banaras Hindu University (BHU), Varanasi, Uttar Pradesh, India
• School of Information and Communication Engineering, Nanjing University of Information Science and Technology (NUIST), Nanjing, P.R. China
• School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, P.R. China
• University School for Advanced Studies (IUSS), Pavia, Italy
• Department of Civil, Building and Environmental Engineering (DICEA) of the University of Napoli Federico II, Napoli, Italy
• School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing, P.R. China
• Jindal Global Business School (JGBS), O.P. Jindal Global University, Sonipat, Haryana, India

Short Summary

This chapter critically assesses the inadequacies of conventional water management in addressing escalating global water crises and advocates for the integration of machine learning, artificial intelligence, and the Internet of Things as essential, adaptive solutions for future water resources modeling, management, and mitigation.

Objective

• To highlight the limitations of traditional water management approaches in the face of dynamic and interconnected contemporary water challenges, including scarcity, pollution, and extreme weather events.
• To propose and conceptually explore the application of machine learning (ML), artificial intelligence (AI), and the Internet of Things (IoT) as innovative and adaptive solutions for real-time prediction and mitigation of water-related risks.

Study Configuration

• Spatial Scale: Global (conceptual discussion of global water challenges and management strategies).
• Temporal Scale: Historical to future (discusses the evolution of water management techniques and the urgent need for future adaptive solutions).

Methodology and Data

• Models used: This chapter conceptually discusses the application of Machine Learning (ML) and Artificial Intelligence (AI) algorithms for water resources modeling and management; no specific models are applied within this chapter.
• Data sources: This chapter conceptually discusses the leveraging of abundant and complex datasets, potentially from IoT devices, for ML/AI applications in water management; no specific data sources are utilized within this chapter.

Main Results

• Conventional water management approaches are insufficient to address contemporary water challenges due to their reliance on manual monitoring, static hydrological models, and reactive intervention strategies.
• Global water crises, including scarcity (affecting over 4 billion people for at least one month per year) and extreme events (e.g., floods causing $82 billion in damage worldwide in 2022), are escalating and demand innovative solutions.
• Machine Learning, Artificial Intelligence, and the Internet of Things offer a paradigm shift for water resources modeling, management, and mitigation by enabling the extraction of patterns and real-time predictions from complex datasets, surpassing traditional statistical methods.

Contributions

• Identifies and articulates the critical limitations of existing water management paradigms in the context of rapid environmental and socio-economic changes.
• Proposes a conceptual framework for integrating advanced technologies (ML, AI, IoT) to develop adaptive, predictive, and proactive solutions for complex water resource challenges.
• Emphasizes the urgent need for a technological shift to enhance the resilience and sustainability of global water systems.

Funding

No specific funding projects, programs, or reference codes are listed in the provided text.

Citation

@article{Singh2026Application,
  author = {Singh, Varsha and Bachar, Dipayon and Karunarathna, R.M. Dinesh Madhushanka and Chaturvedi, Sadashiv and Kumar, Amit and Kumar, Rupesh},
  title = {Application of machine learning/artificial intelligence and IoT in water resources modeling, management, and mitigation},
  journal = {Elsevier eBooks},
  year = {2026},
  doi = {10.1016/b978-0-443-36394-8.00017-0},
  url = {https://doi.org/10.1016/b978-0-443-36394-8.00017-0}
}