Advanced Water Resource Management Using IoT and Big Data Analytics

Effective water resource management is increasingly essential in mitigating the impacts of water scarcity and environmental degradation. This study proposes an integrated system that leverages the Internet of Things (IoT) and Big Data Analytics to enhance efficiency, responsiveness, and sustainability in water governance. The methodology includes real-time data collection through smart sensors, application of statistical and machine learning techniques for predictive modeling, and blockchain-backed data management for transparency. A 30-day simulation involving 50 sensor nodes demonstrated improvements including a 20% enhancement in water quality and a 7% reduction in daily usage. The outcomes validate the viability of this approach, aligning with sustainable development goals and supporting intelligent decision-making in both urban and agricultural contexts.

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Publication Details

Journal
Journal of Computers Mechanical and Management
Published
2025-06-30
DOI
https://doi.org/10.57159/jcmm.4.3.25198
Citations
1
Primary Topic
Water Quality Monitoring Technologies
Type
article
Field-Weighted Citation Impact
0.82
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article

Advanced Water Resource Management Using IoT and Big Data Analytics

S. B. Goyal, Anand Singh Rajawat, Srujan Meesala, Ram Kumar Solanki
1 citations
Journal of Computers Mechanical and Management
Water Quality Monitoring Technologies
0.82
article

Advanced Water Resource Management Using IoT and Big Data Analytics

S. B. Goyal, Anand Singh Rajawat, Srujan Meesala, Ram Kumar Solanki
article en
1 citations

Abstract

Effective water resource management is increasingly essential in mitigating the impacts of water scarcity and environmental degradation. This study proposes an integrated system that leverages the Internet of Things (IoT) and Big Data Analytics to enhance efficiency, responsiveness, and sustainability in water governance. The methodology includes real-time data collection through smart sensors, application of statistical and machine learning techniques for predictive modeling, and blockchain-backed data management for transparency. A 30-day simulation involving 50 sensor nodes demonstrated improvements including a 20% enhancement in water quality and a 7% reduction in daily usage. The outcomes validate the viability of this approach, aligning with sustainable development goals and supporting intelligent decision-making in both urban and agricultural contexts.

Journal of Computers Mechanical and ManagementVol. 4(3)
Sandip Foundation (IN), Malaysia University of Science and Technology (MY), MIT Art, Design and Technology University (IN)
Openalex Percentile: Top 25%
Water Quality Monitoring Technologies
0.82
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Advanced Water Resource Management Using IoT and Big Data Analytics — S. B. Goyal, Anand Singh Rajawat, et al. · Journal of Computers Mechanical and Management (2025) | TGRS Research Map | TGRS