| dc.description.abstract | This paper presents a novel weighted likelihood ensemble approach for predicting pipe failures in water distribution networks (WDNs). The proposed method leverages ensemble modeling, specifically stacking, to enhance prediction capability. The study utilizes a data set of water pipe failures from 2006 to 2017, segmented into different time intervals. Various classification algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB), are employed to predict failures within these segments. These individual models are then combined to create ensemble models. The results show that the stacked models consistently outperform the models that use the training data set as a whole. Along with traditional evaluation metrics, practical assessments are conducted, considering different percentages of pipes for replacement. These evaluations align with tactical and strategic maintenance plans. Remarkably, the most significant improvements are observed in models with lower replacement percentages. The novel aspect of this approach lies in assigning weights to prediction results from different models, each utilizing distinct time segments of data. By developing a meta-model with linear regression based on weighted likelihoods of pipe failures, this method provides valuable insights for asset managers and decision makers. It aids in prioritizing pipe rehabilitation programs, with the potential for further refinement as new failure data becomes available. | |
