Content area

Abstract

Conference Title: 2025 2nd International Conference on Computing and Data Science (ICCDS)

Conference Start Date: 2025 July 25

Conference End Date: 2025 July 26

Conference Location: Chennai, India

AI-driven water asset administration frameworks are changing how we address the developing challenges of water shortage, contamination, and wasteful utilization. With rising request due to populace development, urbanization, and climate alter, conventional water administration approaches frequently drop brief. Fake insights offers a data-driven, proactive elective by joining machine learning, profound learning, and real-time analytics to screen, anticipate, and optimize water utilize. These frameworks use inputs from farther detecting, IoT gadgets, climate information, and chronicled patterns to provide precise estimates and choice back. For occurrence, AI can foresee dry seasons, optimize water system plans in horticulture, and identify spills in urban water systems, all contributing to decreased water squander and progressed proficiency. In country and agrarian settings, AI-powered exactness water system frameworks apply water as it were where and when it is required, moderating assets whereas keeping up efficiency. Urban utilities advantage from keen metering and inconsistency location calculations that offer assistance recognize framework misfortunes rapidly and precisely. Furthermore, AI encourages superior arranging and policy-making by advertising situation modeling and information visualization, which back comprehensive and straightforward administration. In spite of the fact that challenges remain such as information quality, framework crevices, and concerns around algorithmic fairness ongoing progressions in computerized innovations and intrigue collaboration are making a difference to overcome these boundaries. Generally, AI-driven frameworks empower a move from receptive to expectant water administration, making strides strength and supportability. By enabling partners with noteworthy experiences and robotized decision-making apparatuses, these frameworks are basic for guaranteeing impartial and proficient water dissemination in an progressively questionable worldwide environment.

Details

Title
AI-Driven Water Resource Management Systems
Author
Uthayakumar, J 1 ; Swapna 2 ; Ravikumar, A 3 ; Sreeraj, S 4 ; Senthilkumar, R 1 ; Babu Pandipati 5 

 Hindusthan Institute of Technology,Department of CSE,Coimbatore,India 
 Sethu Institute of technology,Department of IT,Karriapatti,India 
 Sridevi Women's Engineering College,Department of CSE,Hyderabad,Telangana 
 Sri Krishna College of Engineering Technology,Department of CSE,Coimbatore,India 
 Geetanjali Institute of Science and Technology,Department of AIDS,Gangavaram, Nellore,A P 
Pages
1-5
Number of pages
5
Publication year
2025
Publication date
2025
Publisher
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Place of publication
Piscataway
Country of publication
United States
Source type
Conference Paper
Language of publication
English
Document type
Conference Proceedings
Publication history
 
 
Online publication date
2025-11-03
Publication history
 
 
   First posting date
03 Nov 2025
ProQuest document ID
3268873131
Document URL
https://www.proquest.com/conference-papers-proceedings/ai-driven-water-resource-management-systems/docview/3268873131/se-2?accountid=208611
Copyright
Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
Last updated
2025-11-06
Database
ProQuest One Academic