TY - JOUR
T1 - Smart Water Management with Digital Twins and Multimodal Transformers
T2 - A Predictive Approach to Usage and Leakage Detection
AU - Ali Syed, Toqeer
AU - Azam Muhammad, Munir
AU - Aziz AlShahrani, Abdul
AU - Hammad, Muhammad
AU - Tayyab Naqash, Muhammad
PY - 2024/12/1
Y1 - 2024/12/1
N2 - Effective water management is crucial in urban and rural settings, requiring efficient usage and timely detection of issues like leakages for sustainability. This paper introduces an integrated framework that combines Digital Twin technology with a multimodal transformer-based model for accurate water usage prediction and leakage detection. The system synchronizes real-time data from various sensors including flow meters, pressure sensors, and thermal imaging devices with a Digital Twin of the water network. Advanced transformer models, specifically the Informer model for long-term time-series prediction and a Water Multimodal Transformer for anomaly detection, process these data to capture complex patterns and dependencies. Experimental results demonstrate the framework’s effectiveness: the Informer model achieved an R2 score of 0.9995 and a Mean Squared Error (MSE) of 2.2, outperforming traditional models. For leakage detection, the model attained 98.4% accuracy and precision, an F1 score of 97.6%, a low False Positive Rate of 0.0019, and an Area Under the Curve (AUC) of 0.984. By fusing diverse sensor data and utilizing advanced transformer architectures, the framework provides a comprehensive view of the water network, enabling real-time decision-making, enhancing forecasting accuracy, and reducing water waste. This scalable solution supports sustainable water management practices in both urban and industrial contexts.
AB - Effective water management is crucial in urban and rural settings, requiring efficient usage and timely detection of issues like leakages for sustainability. This paper introduces an integrated framework that combines Digital Twin technology with a multimodal transformer-based model for accurate water usage prediction and leakage detection. The system synchronizes real-time data from various sensors including flow meters, pressure sensors, and thermal imaging devices with a Digital Twin of the water network. Advanced transformer models, specifically the Informer model for long-term time-series prediction and a Water Multimodal Transformer for anomaly detection, process these data to capture complex patterns and dependencies. Experimental results demonstrate the framework’s effectiveness: the Informer model achieved an R2 score of 0.9995 and a Mean Squared Error (MSE) of 2.2, outperforming traditional models. For leakage detection, the model attained 98.4% accuracy and precision, an F1 score of 97.6%, a low False Positive Rate of 0.0019, and an Area Under the Curve (AUC) of 0.984. By fusing diverse sensor data and utilizing advanced transformer architectures, the framework provides a comprehensive view of the water network, enabling real-time decision-making, enhancing forecasting accuracy, and reducing water waste. This scalable solution supports sustainable water management practices in both urban and industrial contexts.
KW - digital twin
KW - leakage detection
KW - machine learning
KW - multimodal transformers
KW - predictive analytics
KW - real-time monitoring
KW - sensor data fusion
KW - smart water management
KW - water networks
KW - water usage prediction
UR - http://www.scopus.com/inward/record.url?scp=85211926910&partnerID=8YFLogxK
U2 - 10.3390/w16233410
DO - 10.3390/w16233410
M3 - Article
SN - 2073-4441
VL - 16
JO - Water
JF - Water
IS - 23
M1 - 3410
ER -