IoT-Based Water Quality Monitoring and Demand Prediction for Sustainable Management

Abstract

Water resource management has become an increasingly critical issue due to rapid urbanization, population growth, climate variability, and industrial expansion. Ensuring a sustainable water supply requires not only effective distribution but also accurate prediction of future water demand and continuous monitoring of water quality. In this study, we explore the application of Artificial Intelligence (AI) and Machine Learning (ML) models to predict water consumption patterns based on historical data and influential factors such as population dynamics, temperature fluctuations, and rainfall variability. We implemented and evaluated three predictive models—Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) Neural Networks—by comparing their prediction accuracy, computational efficiency, and error metrics including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Among these, the LSTM model outperformed the others, achieving high predictive accuracies of 0.88 for daily, 0.87 for monthly, and 0.86 for yearly forecasts, alongside a significantly low MAE of 1.5 and RMSE of 1.90. The LSTM model’s ability to capture complex temporal dependencies in water usage data demonstrates its suitability for real-world forecasting applications. Furthermore, we propose that the integration of Internet of Things (IoT) technologies—such as sensor networks for real-time monitoring of pH levels, turbidity, flow rates, and pressure—can further enhance the model's responsiveness and predictive capabilities. AI-driven water management frameworks can optimize distribution schedules, detect leaks early, monitor pollution events, and support proactive decision-making, ultimately promoting sustainable urban and rural water systems. Future work will explore hybrid deep learning approaches, cloud-based data analytics, and real-time adaptive control systems to create more intelligent and resilient water management infrastructures. The outcomes of this research offer significant potential for government agencies, water utility companies, and smart city initiatives aiming to achieve sustainable and efficient water resource management in the face of growing environmental and socioeconomic pressures.