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Network design problems appear in many areas such as supply chain management, transportation, telecommunication, and Internet of Things (IoT). Many of such problems can be seen as a problem of finding a subset of links and/or nodes, in order to optimize one or more objectives. The nodes can be cities, telecommunication nodes, IoT sensors, warehouse facilities, and the links between those nodes could be roads, network infrastructure, and routes, respectively.In IoT domain, for example, the architecture commonly consists of a number of sensors, devices, cloud servers, and communication protocols to facilitate a wide range of applications such as transportation, smart city, digital living, and e-agriculture [41]. In effect, a typical IoT or telecommunication infrastructure will relay data from remote connected devices to an application. The coverage, robust-

The presence of floodborne objects (i.e., vegetation, urban objects) during floods is considered a very critical factor because of their non-linear complex hydrodynamics and impacts on flooding outcomes (e.g., diversion of flows, damage to structures, downstream scouring, failure of structures). Conventional flood models are unable to incorporate the impact of floodborne objects mainly because of the highly complex hydrodynamics and non-linear nature associated with their kinematics and accumulation. Vegetation (i.e., logs, branches, shrubs, entangled grass) and urban objects (i.e., vehicles, bins, shopping carts, building waste materials) offer significant materialistic, hydrodynamic and characterization differences which impact flooding outcomes differently. Therefore, recognition of the types of floodborne objects is considered a key aspect in the process of assessing their impact on flooding. The identification of floodborne object types is performed manually by the flood management officials, and there exists no automated solution in this regard. This paper proposes the use of computer vision technologies for automated floodborne objects type identification from a vision sensor. The proposed approach is to use computer vision object detection (i.e., Faster R-CNN, YOLOv4) models to detect a floodborne object’s type from a given image. The dataset used for this research is referred to as the “Floodborne Objects Recognition Dataset (FORD)” and includes real images of floodborne objects blocking the hydraulic structures extracted from Wollongong City Council (WCC) records and simulated images of scaled floodborne objects blocking the culverts collected from hydraulics laboratory experiments. From the results, the Faster R-CNN model with MobileNet backbone was able to achieve the best Mean Average Precision (mAP) of 84% over the test dataset. To demonstrate the practical use of the proposed approach, two potential use cases for the proposed floodborne object type recognition are reported. Overall, the performance of the implemented computer vision models indicated that such models have the potential to be used for automated identification of floodborne object types.

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