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The quality of sensor placement in water distribution systems (WDSs) has considerable impact on leak detection accuracy and identification efficiency. Sensor-placement optimization has long been a major issue in the research of WDS. Conventional hierarchical algorithms based on joint entropy have been widely applied to sensor-placement optimization in the engineering-safety monitoring field. Although such algorithms can effectively reduce information redundancy and are computationally very efficient, they fail to fully account for various practical failure scenarios. Sensor failures introduce bias into monitored data and disrupt the monitoring process, thereby compromising the monitoring performance of a given sensor network. To overcome this drawback, this study proposes a new optimal sensor-placement scheme for WDSs based on an improved hierarchical algorithm. The new optimization scheme works by sequentially selecting sensors using joint entropy as a sensor-placement objective function and fully considers various scenarios of a single-sensor failure to ensure minimal information entropy loss during sensor failure. Simulation is performed on an example network (i.e., Net 3 from EPANET), revealing that when a sensor fails, information entropy loss is 0.011 for conventional sensor-placement schemes, but it is only 0.007 for the improved scheme. Statistical analysis shows that the improved scheme has a higher robustness and adaptability for leak detection and identification, owing to the full consideration of various scenarios of a single-sensor failure.

Extracting levee features (e.g. central lines, levee crowns, levee slopes and sections) has great significance for detecting demaged levees and security assessment. Airborne LiDAR can be utilized to produce continuous surface data DTM.Thus, it has its advantage of lighter workload and higher precision when extracting levee features. In this paper, it is proposed a method to extract levee features with airborne LiDAR-derived DTM. Firstly, central line of levees is generated by ring detection. Compared to conventional methods, ring detection is tolerant to deformated or broken central lines caued by levee erosion or collapsion. Secondly, on this basis, levee crown and slope data is producted according to the standard of levee slope classification. Lastly, equidistant section lines of levees are extracted from ring intersections, and automatically generated the levee section. An empirical research of our method was conducted on 120 km of levees in Gongshuangcha detention basin in the Dongting Lake area, China.This method performed well in accurate extraction of levee engineering information, and has great application potential.

Optimal sensor placement is of great importance for water distribution networks (WDNs). Currently available studies have not considered variations in node contamination probabilities on the WDN. Therefore, this study proposes an optimal sensor placement method for WDNs that considers the variations in node contamination probabilities and the impact of contamination on the network. First, contamination events were selected and clustered according to risk assessment results. Then, based on a hierarchical algorithm, a set of initial sensor placement schemes that meet the identifiability objective of the sensor network was obtained. Subsequently, all placement schemes were compared based on the numbers of sensors and the impact caused by sensor interruptions to obtain an optimal scheme. Tests were performed based on the WDN example model k1. The test results showed that considerations of the variations in node contamination probabilities can ensure the detection and identification of contamination events with high contamination probabilities.

A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%.