IoT-Enabled Water Quality Assessment and Demand Prediction for Sustainable Water Management

Water resource management is a critical challenge in urban planning, requiring accurate demand forecasting to ensure sustainable distribution. This study implements Artificial Intelligence (AI) and Machine Learning (ML) techniques to predict water consumption patterns using historical data and key influencing factors such as population growth, temperature, and rainfall. Three models—Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) Neural Network—were evaluated based on prediction accuracy, error metrics, and computational efficiency. The results indicate that LSTM achieved the highest accuracy, with prediction scores of 0.88 for daily, 0.87 for monthly, and 0.86 for yearly forecasts. Additionally, the model outperformed others in error reduction, achieving a Mean Absolute Error (MAE) of 1.5. The study demonstrates the potential of AI-driven forecasting in optimizing water distribution and conservation strategies. Future research may incorporate real-time IoT sensor data and deep learning models to enhance predictive accuracy and adaptive resource allocation