An Intelligent Blockchain-Enabled System for Secure Water Distribution and Predictive Quality Analysis

Abstract

The increasing demand for secure, transparent, and intelligent resource management systems has encouraged the integration of blockchain technology with machine learning techniques. Efficient water resource management requires accurate tracking of allocation, secure billing processes, and continuous monitoring of water quality parameters to ensure sustainability and public safety. Current water management systems rely heavily on manual or centralized digital processes that primarily focus on basic data storage and operational record keeping. These approaches lack advanced security mechanisms and predictive intelligence, leading to challenges such as limited transparency, risk of data manipulation, delayed verification, poor scalability, and inefficient decision-making. As water resource management becomes more complex, there is a growing need for automated systems that provide secure data handling along with intelligent analytical capabilities. To address these challenges, the proposed system integrates blockchain technology with a machine learning based water quality prediction model within a web-based framework. Blockchain enhances data integrity and transparency by enabling tamper-resistant validation of resource allocation and billing records, while a Random Forest classifier (RFC) predicts water potability using parameters such as pH, conductivity, turbidity, and organic carbon. The system also supports automated billing, payment tracking, resource allocation management, and predictive analysis under a unified architecture. The significance of the proposed system lies in improving operational efficiency, data security, transparency, scalability, and decision-making accuracy, thereby enhancing trust and sustainability in modern water resource management processes.