Multi-level information fusion for spatiotemporal monitoring in water distribution networks

• Multi-level information fusion offers improved water quality monitoring under uncertainty.
• The NF-BRB and high-level BRB systems provide flexible inference frameworks based on WQP features.
• Hybrid learning and dynamic fusion algorithms significantly enhance the detection performance.
• Validation strategy with projecting anomaly signal patterns allows performance evaluation.

This paper deals with online water quality monitoring in distribution networks based on surrogate water quality parameters (WQPs). The present strategy is based on multi-level information fusion using hierarchical belief rule-based (BRB) systems. Networked fuzzy belief rule-based (NF-BRB) and high-level BRB systems are introduced for information fusion at the feature level. Primary and secondary features are extracted from online WQP signals. Primary features are analyzed using the NF-BRB system that is built through knowledge elicitation from experts. Secondary features are interpreted through the high-level BRB system that employs a fuzzy partitioning on the feature sets and a hybrid learning strategy for its rule base construction. Finally, the dynamic fuzzy evidential fusion is introduced to aggregate the local and spatial assessments in each analysis window. As an important contribution of this paper, we propose a new validation method for event detection in the water distribution network (WDN) based on adaptive projection of the signal patterns attributed to anomaly events, obtained through contamination experiments in a pilot facility, to the real WQP signals measured across the WDN. Single and composite contamination events based on several biological and chemical contaminants are simulated to evaluate the performance of the proposed framework in event detection. The proposed multi-level information fusion framework obtains a high detection rate and a reduced number of false negative and positive results.