Smart Water Infrastructure Maintenance Using IoT Data Streams and Deep Learning-Based Time-Series Prediction

Abstract

An accurate prediction of failures is still difficult in smart water infrastructure systems that increasingly make use of IoT-based sensing technologies that provide continuous monitoring; heterogeneous sensor streams, temporal dependencies and noisy real time data. In this study, a deep learning-based predictive maintenance framework is proposed to integrate the IoT data streams with advanced time-series prediction to support intelligent maintenance scheduling, and enhance the reliability of the infrastructure. The whole framework includes IoT sensor data acquisition, data preprocessing, feature engineering, temporal sequence representation and multivariate time-series forecasting using Bidirectional Long Short-Term Memory (BiLSTM) network with attention mechanism. The data from public water infrastructure (SWIF) is used to create the model and to assess it, including flow rate, pressure, vibration, temperature and water quality. The proposed framework is shown to be more effective than the conventional LSTM, GRU, Random Forest and XGBoost models as demonstrated by the experimental results which yielded 96.18% prediction accuracy, 95.42% precision, 95.67% recall, 95.54% F1-score and an RMSE of 0.083, which led to a reduction in maintenance cost by 24.8% and unexpected failure of pipelines by 28.6%. The proposed framework has the potential to improve the predictive maintenance by leveraging strong temporal feature learning, early detection of anomalies and accurate forecasting of asset conditions. The key strengths it brings are the ability to scale its use for smart water infrastructure monitoring, with greater accuracy and proactivity to achieve efficient operation, better use of resources and sustainable solutions for smart water systems.