The steady state of a water distribution system abides by the laws of mass and energy conservation. Hydraulic solvers, such as the one used by EPANET approach the simulation for a given topology with a Newton-Raphson algorithm. However, iterative approximation involves a matrix inversion which acts as a computational bottleneck and may significantly slow down the process. In this work, we propose to rethink the current approach for steady state estimation to leverage the recent advancements in Graphics Processing Unit (GPU) hardware. Modern GPUs enhance matrix multiplication and enable memory-efficient sparse matrix operations, allowing for massive parallelization. Such features are particularly beneficial for state estimation in infrastructure networks, which are characterized by sparse connectivity between system elements. To realize this approach and tap into the potential of GPU-enhanced parallelization, we reformulate the problem as a diffusion process on the edges of a graph. Edge-based diffusion is inherently related to conservation laws governing a water distribution system. Using a numerical approximation scheme, the diffusion leads to a state of the system that satisfies mass and energy conservation principles. Using existing benchmark water distribution systems, we show that the proposed method allows parallelizing thousands of hydraulic simulations simultaneously with very high accuracy.

Small utilities often lack the required amount of data to train machine learning-based models to predict pipe failures, and hence are unable to harness the possibilities and predictive power of machine learning. This study evaluates the generalizability and transferability of a machine learning model to see if small utilities can benefit from the data and models of other utilities. Using nine Norwegian utilities’ datasets, we trained nine global models (by merging multiple datasets) and nine local models (by utilizing each utility's dataset) using random survival forest. Several pre-processing techniques including addressing left-truncated break data and break data scarcity are also presented. The global models and three of the local models were tested to predict the pipe failure of the utilities which were not included in their training datasets. The results indicate that the global models can predict other utilities with sufficient accuracy while local models have some limitations. However, if a representative utility with a sufficiently large (and information rich) dataset is selected, its model can predict the other utility's pipe breaks as accurate as the global models. Furthermore, survival curves for defined cohorts as proxies for uncertainty, and variable importance show that pipes with and without previous breaks behave extremely different. With the understanding of models’ generalizability and transferability, small utilities can benefit from the data and models of other utilities.

The calibration of models for urban drainage systems has become more and more important as especially the usage of detailed models has increased considerably over the last years as the basis for planning and design. Still the effects originating from the choice of data used for model calibration are little known and advice on planning measurement campaigns for model calibration is limited, especially for small and medium-sized municipalities. The choice of measurement sites (number and location) within a sewer system is affecting the robustness of the calibration and in consequence the assessment of the modelled system behaviour. This paper discusses the calibration of a hydrologic-hydrodynamic model using the representative example of a small municipality. Different calibration scenarios were created using a model-based approach, focusing on varying availability of in-sewer measurement data. To assess the performance of different scenarios and validate the respective models, different model outputs were compared. The different calibration scenarios resulted in high variations in the model performances. The number and location of used calibration points influence model performance significantly. Predicted CSO volumes deviate from a set of given reference values in ranges between 1% and 253% for one, −21% to −5% for two and 1% to 237% for five used calibration points, depending on the rainfall data input. Consequently, the design of measurement campaigns for calibration data is a very sensitive decision in the modelling process. The model performance further influences design and decision-making processes, which are then perceptible in economic and functional aspects.

Pipe failure modelling is an important tool for strategic rehabilitation planning of urban water distribution infrastructure. Rehabilitation predictions are mostly based on existing network data and historical failure records, both of varying quality. This paper presents a framework for the extraction and processing of such data to use it for training of decision tree-based machine learning methods. The performance of trained models for predicting pipe failures is evaluated for simple as well as more advanced, ensemble-based, decision tree methods. Bootstrap aggregation and boosting techniques are used to improve the accuracy of the models. The models are trained on 50% of the available data and their performance is evaluated using confusion matrices and receiver operating characteristic curves. While all models show very good performance, the boosted decision tree approach using random undersampling turns out to have the best performance and thus is applied to a real world case study. The applicability of decision tree methods for practical rehabilitation planning is demonstrated for the pipe network of a medium sized city.