AI-Powered Sensor Network for Real-Time Water Quality Monitoring

Thesis Abstract

Water quality monitoring is essential for safeguarding public health, preserving aquatic ecosystems, and supporting sustainable resource management; however, traditional methods are often characterized by delayed results, high operational costs, and limited spatial coverage. Addressing these challenges, this study aims to develop and evaluate an artificial intelligence (AI)-driven sensor network capable of providing real-time water quality data with high accuracy and operational efficiency. The specific objectives include designing an integrated sensor system for continuous water parameter measurement, implementing machine learning algorithms for data processing and anomaly detection, and assessing the deployment’s effectiveness in diverse environmental conditions. The research employs a quantitative, descriptive research design, targeting water bodies within a metropolitan area with diverse industrial, agricultural, and residential influences, with a sampling frame comprising 50 strategic monitoring sites. A purposive sampling technique is applied to select these sites based on pollution risk profiles and accessibility. Data collection involves deploying IoT-enabled sensors equipped with multi-parametric probes to measure key water quality indicators such as pH, dissolved oxygen, turbidity, heavy metals, and microbial counts over a 12-month period, complemented by laboratory analyses for calibration and validation. Data obtained are processed using a hybrid analytical framework incorporating supervised machine learning models—specifically Random Forest and Support Vector Machines—to classify water quality status and detect anomalies, along with statistical techniques such as regression analysis and ANOVA to examine spatial and temporal patterns. The study adopts the Theory of Planned Behavior and the Systems Theory as guiding frameworks to understand user acceptance and the technological integration within environmental monitoring systems. Expected findings include high accuracy of AI models in identifying water quality deviations, enhanced detection sensitivity compared to conventional methods, and improved decision-making efficiency through real-time data dissemination. The results will demonstrate that the AI-powered sensor network significantly outperforms traditional monitoring approaches in terms of timeliness, cost-effectiveness, and scalability, providing a replicable model for environmental agencies and policymakers. This research makes a substantial contribution to knowledge by integrating advanced IoT and AI techniques into environmental monitoring practices, thus bridging technological gaps and promoting sustainable water quality management. It also offers insights into the operational challenges and user acceptance factors associated with deploying intelligent sensor systems in complex environmental settings. The study concludes that AI-enabled sensor networks are pivotal for proactive water quality management and recommends further research on integrating predictive analytics, expanding sensor capabilities for broader pollutant detection, and exploring community-based deployment models to enhance participatory environmental governance. Overall, this work underscores the transformative potential of ICT-driven solutions in environmental sciences and provides a strategic framework for stakeholders seeking to implement scalable, real-time water monitoring systems that align with global sustainability goals.