# GIS-Based Identification of Locations in Water Distribution Networks Vulnerable to Leakage
###### Abstract
This characterization implies that it is often difficult to extrapolate model-based results from one water main to another [8]. Accurate models must consider static factors (such as pipe material and soil time) and time-dependent variables (such as pipe age, soil temperature, and water temperature). These considerations increase the complexity of the resulting model. Measurement-based methods require live measurements acquired from field surveys and using instruments on-site to assess the presence and location of leakages, such as radioactive approach, acoustics logging approach, Ground-Penetrating Radar (GPR), and Infrared (IR) thermography [9; 10]. Other measurement-based detection methods include acoustic monitoring, pressure analysis, volume balance measurements, and negative pressure evaluations [11]. The data requirements associated with measurement-based models create a significant limitation in practical applications, as such methods require extensive in-field inspections and surveys, which require time and vast resources [8]. The implementation of measurement-based approaches increases the cost of WDN management. Physical measurement-based methods also face limitations in providing continuous failure monitoring and rapid leak detection. Complex WDNs will require sophisticated measurement equipment. Acoustic measurements require many sensors, which increases labor requirements [11]. A study by [12] identified sensor placement as an essential factor influencing accurate leak detection. Labor-intensive leak detection methods result in extended detection and location times. This observation stems from infrequent leak inspections. Measurement-based methods reduce the likelihood of a quick response. The measurement of every network node and branch is not financially feasible. Other leak detection methods include Geographical Information Systems (GIS) and remote sensing-based tools that can be adopted in large and complex WDNs. The remote sensing method involves investigating and studying the physical features in a particular area by evaluating its reflected wavelengths measured by terrestrial, airborne, or satellite sensors. In some cases, the Normalized Difference Vegetation Index (NDVI) is used to process remotely sensed data and images to detect water leakage locations. Sometimes, field spectroscopy reinforces remote-sensing-based outcomes because low spatial resolution images may not be sufficient to detect small leakages [5]. Detection systems can deploy three remote sensing methods: ground, arial, and satellite [13]. The advantages associated with ground-based remote sensing include continuous leak monitoring and high spatial resolution. However, this technology faces limitations resulting from low spatial coverage. This characteristic implies that complex WDNs will require a network of sensors, which increases detection costs. Ground-based remote sensing equipment is exposed to the environment and requires frequent maintenance [13]. The benefits of aerial remote sensing include rapid deployment and excellent ground coverage relatively quickly. However, environmental and weather factors influence aerial deployment. Aerial sensing does not offer continuous monitoring and is expensive to implement and maintain [13]. Finally, satellite remote sensing provides unmatched ground coverage on continental and global scales. However, low resolution can hamper leak source identification. The weather affects some satellite remote sensing capabilities, except RADAR [14]. Deploying satellite remote sensing for smaller networks is not economically feasible, since this system requires high capital investment. GIS manages, stores, analyzes, and displays georeferenced information [15]. GIS algorithms aid in the modeling, visualization, analysis, and management of WDN [16]. It can, therefore, help in the decision-making process by presenting detailed and specific information regarding a location with a suspected leakage.