A novel approach for identifying the source of contamination in a water supply network based on the random forest classifying algorithm is presented in this paper. The proposed method is tested on two different water distribution benchmark networks with different sensor placements. For each considered network, a considerable amount of contamination scenarios with randomly selected contamination parameters were simulated and water quality time series of network sensors were obtained. Pollution scenarios were defined by randomly generated pollution source location, pollution starting time, duration of injection and the chemical intensity of the pollutant. Sensor layout's influence, demand uncertainty and imperfect sensor measurements were also investigated to verify the robustness of the method. The proposed approach shows high accuracy in localizing the potential sources of pollution, thus greatly reducing the complexity of the water supply network contamination detection problem.

This work investigates mixing phenomena in a pressurized pipe system with two sequential Tee junctions and experiments are conducted for a range of different inlet flow ratios, varying distances between Tee junctions and two pipe branching configurations. Additionally, obtained experimental results are compared with results from previous studies by different authors and are used to validate the numerical model using the open source computational fluid dynamics toolbox OpenFOAM. Two different numerical approaches are used—Passive scalar model and Multiphase model. It is found that both numerical models produce similar results and that they are both greatly dependent on the turbulent Schmidt number. After the calibration procedure, both models provided good results for all investigated flow ratios, double-Tee junction distances, and pipe branching configurations, therefore both numerical models can be applied for a wide range of pipe networks configurations, but passive scalar model is the viable choice due to its much higher computational efficiency. Obtained results also describe the relationship between the double-Tee distances and complete mixing occurrence.

No abstract

No abstract

In this paper, a novel machine learning based algorithm for water supply pollution source identification is presented built specifically for high performance parallel systems. The algorithm utilizes the combination of Artificial Neural Networks for classification of the pollution source with Random Forests for regression analysis to determine significant variables of a contamination event such as start time, end time and contaminant chemical concentration. The algorithm is based on performing Monte Carlo water quality and hydraulic simulations in parallel, recording data with sensors placed within a water supply network and selecting a most probable pollution source based on a tournament style selection between suspect nodes in a network with mentioned machine learning methods. The novel algorithmic framework is tested on a small (92 nodes) and medium sized (865 nodes) water supply sensor network benchmarks with a set contamination event start time, end time and chemical concentration. Out of the 30 runs, the true source node was the finalist of the algorithm’s tournament style selection for 30/30 runs for the small network, and 29/30 runs for the medium sized network. For all the 30 runs on the small sensor network, the true contamination event scenario start time, end time and chemical concentration was set as 14:20, 20:20 and 813.7 mg/L, respectively. The root mean square errors for all 30 algorithm runs for the three variables were 48 min, 4.38 min and 18.06 mg/L. For the 29 successful medium sized network runs the start time was 06:50, end time 07:40 and chemical concentration of 837 mg/L and the root mean square errors were 6.06 min, 12.36 min and 299.84 mg/L. The algorithmic framework successfully narrows down the potential sources of contamination leading to a pollution source identification, start and ending time of the event and the contaminant chemical concentration.