Fractal-Heuristic Method of Water Quality Sensor Locations in Water Supply Network

Abstract

The article presents a new methodology for the selection of the water quality monitoring sensor locations using the water quality model created by means of the EPANET 2.0 software (United States Environmental Protection Agency (USEPA), Durham, NC, USA). The model represents the propagation of free chlorine in a water supply network, in conjunction with the heuristic method, applying the elements of fractal geometry. In the first stage, a subarea is determined, while in the second, a specific node for the location of the measuring point is indicated. The process of determining the location is based on a ranking method, in which the usefulness of individual subareas and measuring nodes is determined by means of a formula taking into account the amount of water intake, the required level of water supply and the effects of a lack of water supply, as well as the calculated concentration of free chlorine. The application of the method requires the construction and calibration of a numerical model of free chlorine decay in the network, as well as the knowledge on the location of the objects of particular importance for the network users. The proposed method will be applied in an existing water supply network of approximately 50,000 recipients.

Placement Strategies for Water Quality Sensors Using Complex Network Theory for Continuous and Intermittent Water Distribution Systems

“…The basic metrics for evaluating the quality of the sensor placement are the four objective functions Z 1– Z 4 (Ostfeld et al., 2008). The Threat Ensemble Vulnerability Assessment—Sensor Placement Optimization Tool (TEVA‐SPOT) simulation tool (Janke, 2017; Janke et al., 2006, 2012) from US EPA is popular in literature to simulate diverse intrusion scenarios and calculate these metrics for the proposed sensor locations in the WDN (Beata et al., 2020; Khorshidi et al., 2018; Hart et al., 2008; J. Berry et al., 2010; Schal et al., 2013; Giudicianni et al., 2020). It uses the input hydraulic model of the WDS from EPANET (L. A. Rossman, 2000).…”

An Interval Type-3 Fuzzy–Fractal Approach for Plant Monitoring

“…Recently, significant progress has been realized in comprehending the complexity of an object through the utilization of fractal constructs [21]. For example, time series in finance and engineering exhibit properties suggesting a fractal structure [22,23]. In addition, applications in medicine (like in COVID-19), robotics, control, and others can be found in the recent literature [24].…”

“…More recently, type-2 has also been considered in this area, as a way to model uncertainty in a better fashion and achieve better results, as can be verified in [13,14]. In addition, there are also works where fractal theory has been solely applied for achieving efficient monitoring of complex systems, like the works presented in [15][16][17][18][19][20][21][22][23]. In this case, fractal theory constructs are utilized to analyze the complexity of monitoring data to find hidden structure in the data.…”

Escape Criteria Using Hybrid Picard S-Iteration Leading to a Comparative Analysis of Fractal Mandelbrot Sets Generated with S-Iteration

“…To understand and predict violent streams, fractals are also used in liquid mechanics. Fractal geometries play a crucial role in determining where water quality sensors should be placed in the water delivery network [2] and fractal river network [3]. Additionally, fractal theory is frequently applied in several fields, including engineering models, video compression [4], and computational architectural design [5].…”