AI-powered IoT solutions for sustainable water management in cities

Authors

  • Priyanshu Jana * School of Computer Engineering, KIIT (Deemed to be) University, Bhubaneswar -751024, Odisha, India

https://doi.org/10.48313/uda.v1i2.37

Abstract

Water management is one of the most important subjects in most international conferences. Water collection and recycling are the primary prerequisites for meeting the impending need for water, a widespread water problem worldwide. More focus on water management strategies used in various application areas is necessary to accomplish this. Implementing intelligent water management mechanisms is vital for efficient distribution, conservation, and upholding water quality standards for various uses while considering the population density index. A few key application areas that are necessary for effective water management are covered in the assigned assignment. These are current developments in water distribution, rainwater collection, irrigation management, wastewater recycling, and using different Artificial Intelligence (AI) models.

Additionally, the data collected for these applications varies by type and is unique. Therefore, it is imperative to employ a model or algorithm that can be used to produce solutions for each of these applications. The Internet of Things (IoT) framework, in conjunction with AI and Deep Learning (DL) approaches, can help create a smart water management system for sustainable water utilization from natural resources. This study examines several water management strategies and develops a practical framework for water management by utilizing AI/DL, the IoT network, case studies, and sample statistical analysis.

Keywords:

Water management, Smart cities, Internet of things, Water management strategies

References

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Published

2024-12-06

Issue

Vol. 1 No. 2 (2024): Uncertainty Discourse and Applications

Section

Articles

How to Cite

AI-powered IoT solutions for sustainable water management in cities. (2024). Uncertainty Discourse and Applications, 1(2), 158-169. https://doi.org/10.48313/uda.v1i2.37

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