Socioeconomic characteristics are influencing the temporal and spatial variability of water demand, which are the biggest source of uncertainties within water distribution system modeling. Improving current knowledge of these influences can be utilized to decrease demand uncertainties. This paper aims to link smart water meter data to socioeconomic user characteristics by applying a novel clustering algorithm that uses a dynamic time warping metric on daily demand patterns. The approach is tested on simulated and measured single-family home data sets. It is shown that the novel algorithm performs better compared with commonly used clustering methods, both in finding the right number of clusters as well as assigning patterns correctly. Additionally, the methodology can be used to identify outliers within clusters of demand patterns. Furthermore, this study investigates which socioeconomic characteristics (e.g., employment status and number of residents) are prevalent within single clusters and, consequently, can be linked to the shape of the cluster’s barycenters. In future, the proposed methods in combination with stochastic demand models can be used to fill data gaps in hydraulic models.@en

Sewer pipes are commonly inspected in situ with CCTV equipment. The CCTV footage is then reviewed by human operators in order to classify defects in the pipes and make a recommendation on possible interventions. This process is both labor-intensive and error-prone. Other researchers have suggested machine learning techniques to (partially) automate the human review of this footage, but the automated classifiers are often validated in artificial testing setups, leading to biased results that do not translate directly to operational impact. In this work, we discuss suitable evaluation metrics for this specific classification task — most notably ‘specificity at sensitivity’ and ‘precision at recall’ — and the importance of using a validation setup that includes a realistic ratio of images with defects to images without defects, and a sufficiently large dataset. We also introduce ‘leave-two-inspections-out’ cross validation, designed to eliminate a data leakage bias that would otherwise cause an overestimation of classifier performance. We designed a convolutional neural network (CNN) and applied this validation methodology to automatically detect the twelve most common defect types in a dataset of over 2 million CCTV images. With this dataset and our validation methodology, our CNN outperforms the state-of-the-art. Classification performance was highest for intruding and defective connections and lowest for porous pipes. While the CNN is not capable of fully automated classification at sufficient performance levels, we determined that if we augment the human operator with the CNN, this may reduce the required human labor by up to 60.5%.@en