Feature-DTW and Mel-Spectrogram-based sensor fusion: A framework for leak detection and classification in water distribution systems

Water leak detection is essential to prevent water, energy, and financial losses while ensuring a reliable supply. In this context, the present work uses a laboratory-based leak dataset and proposes an efficient framework for leak detection and classification, combining advanced feature engineering with sensor fusion and machine learning techniques. The method employs Dynamic Time Warping (DTW) for comparison with a reference-instance strategy within a refined Feature-DTW framework that uses Mel-Spectrograms for time-series representation. This approach reduces computational cost through low-dimensional spectrogram representation and enhances feature richness by extracting attributes from the DTW alignment path. Three sensor fusion strategies are proposed and evaluated, integrating multi-sensor data with trade-offs between accuracy and computational efficiency. Using the extreme gradient boosting algorithm, the framework achieves high performance in both leak detection and leak type classification. The method attained an accuracy of 0.991 for leak detection and 0.984 for leak type classification — matching state-of-the-art results while applying multi-sensor techniques and maintaining a computational footprint suitable for real-time deployment on edge devices. The results demonstrate that spectrogram-based Feature-DTW, when combined with targeted reference instances and efficient fusion strategies, is a powerful and scalable approach for accurate leak detection and classification. • A sampling strategy that designates reference instances for similarity comparisons. • A pre-processing pipeline for optimal frequency filtering. • Mel-Spectrogram as a computationally efficient representation of time series data. • Proposal and comparative testing of three distinct sensor fusion methods via DTW. • Expanded feature set through incorporation of DTW warping path attributes.