A Context-Aware Progressive Approach to Imputing Multivariate Heterogeneous Data in Water Pipe Networks

Given the logistical and financial complications in routine inspection of water pipelines, data-driven forecasting models play a crucial role in prioritizing inspection and maintenance tasks for pipes with a higher probability of failure. These models rely on extensive data sets that include pipe characteristics, external factors, and historical failure records. Due to several reasons, including the age of water networks and inadequate documentation systems, pipeline data sets often contain missing or incomplete records. These gaps may significantly reduce the predictive power and reliability of forecasting models. As a result, robust data imputation methods that estimate missing values and reconstruct data sets are essential. However, the few existing solutions that address this challenge face critical limitations, including low accuracy and an inability to handle heterogeneous data that involve both numerical and categorical variables. To overcome these issues, this study introduces a novel progressive method for multivariate heterogeneous data imputation tailored to water pipeline data sets. The proposed method uses a deep learning-based progressive forecasting approach using a gated recurrent unit (GRU) autoencoder and an extreme gradient boosting (XGBoost) to estimate missing values by capturing complex associations and interdependencies among variables. To evaluate the effectiveness of the proposed method, it was applied to real-world data from Calgary, Canada, to estimate missing values for three key pipe attributes: pipe diameter, pipe material, and pipe installation year. The results demonstrated that the proposed method achieves strong predictive performance and significantly outperforms benchmark methods, such as k-nearest neighbors (KNN), multivariate imputation by chained equations (MICE), and MissForest.