## 4 Bigram and Traditional Results
### Advanced Leak Detection in Water Networks
#### 4.4.2 Traditional Analysis
In [78], a leakage detection model for water supply networks called DBSCAN-MFCN is presented, which combines DBSCAN and multiscale fully convolutional networks (MFCN). By dividing the network into zones and using them as learning labels, the model improves efficiency and accuracy in leak detection, reducing water loss compared to other methods. The model detects leakage areas and is more accurate than support vector machine (SVM), Naive Bayes Classifier (NBC), and k-Nearest Neighbor (KNN), with improvements of 78%, 72%, and 28%, respectively. Another relevant point, besides the use of ML, is the system's sensorization. In [80], a water pipeline leakage detection method is proposed based on machine learning and wireless sensor networks. The system reduces energy consumption and improves leak detection accuracy using signal analysis and a support vector machine. The results show that the method effectively identifies leaks and consumes less energy than conventional methods. Another methodology used in these problems corresponds to the Bayesian temporal reasoning approach, which has been effective in enhancing leak localization accuracy within water networks. In [81], this approach is combined with linear discriminant analysis and neural network techniques. Preliminary results from a case study demonstrate an accuracy rate of over 80%. In [82], a time-frequency convolutional neural network (TFCNN) model is also used to identify leakage signals in water networks, outperforming other classification models and showing an average accuracy of 98% under various noise conditions. The detection accuracy reaches 99% in practice, making the transfer learning-based TFCNN a promising approach, especially when dealing with limited data sets. In [83], the authors use machine learning to present an acoustic leak detection system for water pipes. The proposed solution employs a joint learning approach called multi-strategy ensemble learning (MEL) with a gradient boosting tree (GBT) classification model, improving performance and significantly reducing false positives compared to other models. A unique methodology for predicting leak shapes in water pipes is presented in [84]. This methodology uses the vibration signal generated by the leak noise. The research employs an innovative signal processing technique that combines machine learning with Random Forest classifiers and various signal features to develop a leak shape prediction algorithm. The results demonstrate a robust method for predicting leak shapes at different flow rates and backfill types, providing a valuable tool for water companies to assess leak repairs based on the leak shape. In [85], a fast hybrid method is proposed, using Random Forest, KNN, Bayesian network algorithms, and hydraulic relations to detect and locate leaks in water distribution networks. The approach relies on cost-effective flow sensors and hydraulic equations for leak detection. The results show that the method outperforms other existing methods. A novel unsupervised RNN model for leakage detection and location was introduced in [99], achieving a detection sensitivity of 97% using pressure data and 100% using flow data, with true leak zone identification for 95% of scenarios.