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A pipeline leak localization technique is crucial in a structural health monitoring system to prevent water wastage at an early stage. Recent advancements include machine learning and deep learning techniques to detect a leak with great accuracy, and cross-correlation is the widely used tool for localization. All the existing algorithms use two architectures to detect and localize a leak. The main aim of this paper is to propose a standalone architecture for leak detection and localization with 1DCNN and novel AdaBoost architecture. The data collected from a real-time pipeline setup is redundant, very dynamic, and contains a substantial amount of surrounding noise. So an appropriate feature extraction technique helps to capture essential information from pipeline data. In the proposed method, the 1DCNN extracts the essential features from the data collected from the real-time pipeline setup located in UTAR, Malaysia, using an Acousto-optic vibration sensor. The proposed novel AdaBoost architecture localizes the leak with the help of the features extracted using 1DCNN. The performance metrics used in this paper to evaluate the proposed method are training accuracy, validation accuracy, precision, recall, F1 score, and R-squared, and the results are 99.23, 97.26, 98.18, 98.19, 98.14, and 98.55, respectively which is better compared to the implemented existing method. The results show that the proposed 1DCNN-novel AdaBoost localizes the leak with high training and validation accuracy and performs well in other metrics.

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