To detect pipe burst locals, a study developed a burst location identification framework by fullylinear DenseNet (BLIFF) (Figure ) , which relies on deep learning through the fully-linear DenseNet (FL-DenseNet) model. BLIFF supplants the convolutional layers in DenseNet with linear connections and omits pooling layers. Using real-time pressure measurements as the inputs, BLIFF generates the likelihood values of a burst for each pipe in the potential burst district. The prediction accuracies, ranging from 62.35% (highest probability pipe match) to 98.58% (top five pipes match), of BLIFF are two times those of the original DenseNet model. The remarkable improvement in the prediction accuracy is attributed to the linearconnection layer, which discerns global features in the pressure signals. The effective deployment of deep learning methods such as BLIFF corroborates the viability of pressure values in burst localization, countering prior assertions of their insensitivity to burst events . Another work proposed an advanced meta-learning (AdvaML) model to predict the failure of water pipelines . AdvaML comprises an input layer with 33 neurons (mirroring the 33 input variables including pipe and climate data), four hidden layers, and an output layer that yields the failure/hazard index of a pipe. AdvaML forecasts the risk index associated with pipe failures and detects pivotal determinants of pipeline service life. Of these determinants, the number of traffic lanes and chlorine residual concentration are paramount, collectively contributing approximately 9% to the service life analysis of water pipes. Benefiting from its knowledge transfer from initial parameterization to the ultimate learning phase, AdvaML has commendable performance even with scant training data compared with cox-proportional hazards (Cox-PH), survival support vector machine (SSVM), and random survival forest (SRF).