ALBERT

Description: Water, Vol. 9, Pages 894: Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems Water doi: 10.3390/w9110894 Authors: Jie Yu Le Xu Xiang Xie Dibo Hou Pingjie Huang Guangxin Zhang Hongjian Zhang As a core part of protecting water quality safety in water distribution systems, contamination event detection requires high accuracy. Previously, temporal analysis-based methods for single sensor stations have shown limited performance as they fail to consider spatial information. Besides, abundant historical data from multiple stations are still underexploited in causal relationship modelling. In this paper, a contamination event detection method is proposed, in which both temporal and spatial information from multi-stations in water distribution systems are used. The causal relationship between upstream and downstream stations is modelled by Bayesian Network, using the historical water quality data and hydraulic data. Then, the spatial abnormal probability for one station is obtained by comparing its current causal relationship with the established model. Meanwhile, temporal abnormal probability is obtained by conventional methods, such as an Autoregressive (AR) or threshold model for the same station. The integrated probability that is calculated employed temporal and spatial probabilities using Logistic Regression to determine the final detection result. The proposed method is tested over two networks and its detection performance is evaluated against results obtained from traditional methods using only temporal analysis. Results indicate that the proposed method shows higher accuracy due to its increased information from both temporal and spatial dimensions.

Description: Sensors, Vol. 18, Pages 938: Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters Sensors doi: 10.3390/s18040938 Authors: Kexin Wang Xiang Wen Dibo Hou Dezhan Tu Naifu Zhu Pingjie Huang Guangxin Zhang Hongjian Zhang In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems.