###### Abstract  
Significant water loss caused by pipeline leaks emphasizes the importance of effective pipeline leak detection and localization techniques to minimize water wastage. All of the state-of-the-art approaches use deep learning (DL) for leak detection and cross-correlation for leak localization. The existing methods' complexity is very high, as they detect and localize the leak using two different architectures. This paper aims to present an independent architecture with a single sensor for detecting and localizing leaks with enhanced performance. The proposed approach combines a novel EMD with an optimal mode selector, an MFCC, and a two-dimensional convolutional neural network (2 DCNN). The suggested technique uses acousto-optic sensor data from a real-time water pipeline setup in UTAR, Malaysia. The collected data are noisy, redundant, and a one-dimensional time series. So, the data must be denoised and prepared before being fed to the 2 DCNN for detection and localization. The proposed novel EMD with an optimal mode selector denoises the one-dimensional time series data and identifies the desired IMF. The desired IMF is passed to the MFCC and then to 2 DCNN to detect and localize the leak. The assessment criteria employed in this study are prediction accuracy, precision, recall, F-score, and R-squared. The existing MFCC helps validate the proposed method's leak detection-only credibility. This paper also implements EMD variants to show the novel EMD's importance with the optimal mode selector algorithm. The reliability of the proposed novel EMD with an optimal mode selector, an MFCC, and a 2 DCNN is cross-verified with cross-correlation. The findings demonstrate that the novel EMD with an optimal mode selector, an MFCC, and a 2 DCNN surpasses the alternative leak detection-only methods and leak detection and localization methods. The proposed leak detection method gives 99.99% accuracy across all the metrics. The proposed leak detection and localization method's prediction accuracy is 99.54%, precision is 98.92%, recall is 98.86%, F-score is 98.89%, and R-square is 99.09%. EMD; MFCC; 2 DCNN; leak detection and localization; acousto-optic sensor 202331 October 202324 November 202328 November 20231 December 202331 December 202331 October 202324 November 202328 November 20231 December 20231 December 202328 November 20231 December 202328 November 20231 December 202328 November 202328 November 2  
pipeline leak led to the wastage of a substantial amount of water, measuring many lakh litters [5]. Further, another pipeline burst in a high-pressure water pipeline led to the entrapment of three people in Assam, India [6]. These incidents show that the water pipeline leak should be attended to as early as possible to minimize wastage with appropriate leak detection and localization methods. The categories of pipeline leak detection and localization methods are exterior methods, interior methods, and visual or biological-based methods [7]. Visual or biological methods help to locate the leak more precisely, but they need constant and frequent monitoring. The exterior methods involve sensing devices to monitor the outer parts of the pipeline, whereas the interior methods deal with monitoring the inner parts of the pipeline. The exterior and interior methods give more precise detection and localization results but are more costly. A few monitoring devices used in pipeline leak detection and localization are acoustic emission sensors [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19], infrared thermography [20; 21], microphones [22], CCTV, vibration sensors [23; 24], accelerometers [25], pressure sensors [26; 27; 28; 29], flow meters [30; 31], and pipeline robots [32]. The acoustic emission and vibration sensors capture the vibration in the pipeline. Microphones record the sound created by the leak. At the same time, the flow meters estimate the amount of water flow. Infrared thermography captures the energy dissipated. CCTV captures the state of the issue through images.