Decision of Water Quality Measurement Locations for the Identification of Water Quality Problems under Emergency Link Pipe Operation  
[PERSON]  
Department of Civil Engineering, The University of Suwon, H However, the installation of measuring instruments in all pipelines has a number of limitations, such as maintenance costs and, in particular, water quality instruments are relatively difficult to understand, install, and operate due to the multiple detection items and inspection cycles compared to a flowmeter. To overcome this limitation, [PERSON] et al. [4] and [PERSON] et al. [5] proposed methodologies to detect water quality problems by constructing a matrix of pipeline contamination. Recently, as computer performance and data literacy have improved, [PERSON] and [PERSON] [6] proposed a methodology to detect water problems by applying topological algorithms, and [PERSON] et al. [7], [PERSON] et al. [8] decided optimal water quality measurement points using a greedy algorithm. In addition, [PERSON] and [PERSON] [9], [PERSON] and [PERSON] [10] applied the multi-objective Non-dominated Sorted Genetic Algorithm-II (NSGA-II) proposed by [11]. Most of the previous literature used fully advection models for water quality analysis. In recent studies, inadequate problems of numerical models [12] and uncertainty issues [13; 14; 15], such as sensor measurements and model calibration errors, were raised and tackled. As described above, during the last decade research on the location of water quality instruments has been conducted in various detailed fields such as decision procedure proposals, improvement of hydraulic and water quality analysis techniques, and consideration of uncertainties [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. However, it is difficult to determine the superiority of the results among these previous studies since the results vary depending on the purpose. Also, research on deciding the water quality measurement points has been focused on man-made accidents with high damage but low probability of occurrence, such as terrorism.  
In contrast, [PERSON] et al. [27] suggested water quality measurement point location by utilizing graph theory and a multi-objective genetic algorithm (MOGA) in consideration of scenarios that can occur commonly, such as single pipeline damage and demand fluctuations. However, the study did not consider situations that could cause large-scale water quality accidents such as water system conversion, and just applied to the simple example network included in EPANET2 software [28]. Considering the cases of pipeline water quality accidents that have occurred on site so far, water quality degradation in pipelines is highly likely to be caused by a change of flow direction. If a single direction of flow in a pipeline is reversed due to a sudden abnormal situation, the sediments that have accumulated inside the pipeline can be swept away and increase turbidity. In order to solve this problem, [PERSON] et al. [29] proposed a flushing method, that is, a pipe-cleaning technique that uses the flow rate generated when the water inside the pipeline is discharged to the outside by the operation of a valve. However, there is a limit to applying the flushing method to all locations, so most countries do not use this as a tool for regular cleaning. Therefore, there is a need to preemptively determine and manage the pipelines where water pollution can occur and, for this purpose, it is necessary to determine water quality measurement locations in consideration of the possible situations that can arise. In this study, we proposed a methodology for selecting water quality measurement points, considering the sensitivity of the water flow direction by setting a water system conversion due to an emergency situation between water supply areas as the representative abnormal scenario. The proposed methodology was applied to a new city, A, in Korea, and the results are analyzed in detail and presented.