Floods are one of the most often occurring and damaging natural hazards. They impact the society on a massive scale and result in significant damages. To reduce the impact of floods, society needs to keep benefiting from the latest technological innovations. Drones equipped with sensors and latest algorithms (e.g., computer vision and deep learning) have emerged as a potential platform which may be useful for flood monitoring, mapping and detection activities in a more efficient way than current practice. To better understand the scope and recent trends in the domain of drones for flood management, we performed a detailed bibliometric analysis. The intent of performing the bibliometric analysis waws to highlight the important research trends, co-occurrence relationships and patterns to inform the new researchers in this domain. The bibliometric analysis was performed in terms of performance analysis (i.e., publication statistics, citations statistics, top publishing countries, top publishing journals, top publishing institutions, top publishers and top Web of Science (WoS) categories) and science mapping (i.e., citations by country, citations by journals, keyword co-occurrences, co-authorship, co-citations and bibliographic coupling) for a total of 569 records extracted from WoS for the duration 2000–2022. The VOSviewer open source tool has been used for generating the bibliographic network maps. Subjective discussions of the results explain the obtained trends from the bibliometric analysis. In the end, a detailed review of top 28 most recent publications was performed and subjected to process-driven analysis in the context of flood management. The potential active areas of research were also identified for future research in regard to the use of drones for flood monitoring, mapping and detection activities.

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.

This study examines an innovative Coastal Reservoir (CR) technique as a feasible solution for flood adaptation and mitigation in the Brisbane River Estuary (BRE), Australia, which is vulnerable to coastal flooding. The study analysed the operation of a CR by using the MIKE 21 hydrodynamic modelling package. The 2D hydrodynamic model was calibrated and validated for the 2013 and 2011 flood events respectively, with a Nash-Sutcliffe coefficient (E_ns) between 0.87 to 0.97 at all gauges. River right branch widening and dredging produced a 0.16 m reduction in water level at the Brisbane city gauge. The results show that by suitable gate operation of CR, the 2011 flood normal observed level of 4.46 m, with reference to the Australian Height Datum (AHD) at Brisbane city, could have been reduced to 3.88 m AHD, while under the improved management operation of the Wivenhoe Dam, the flood level could be lowered to 4 m AHD at Brisbane city, which could have been reduced with CR to 2.87 m AHD with an overall water level reduction below the maximum flood level. The results demonstrated that the innovative use of a CR could considerably decrease the overall flood peak and lessen flood severity in the coastal city of Brisbane.

Coastal waters, particularly in micro-tidal estuaries, are highly vulnerable to water quality changes driven by salinity intrusion. Effective coastal water management requires a detailed understanding of the dynamic processes in estuaries to mitigate the effects of salinity intrusion. This study focuses on the Brisbane River estuary (BRE), Australia, to investigate salinity intrusion and flushing time under varying freshwater inflows. A coupled MIKE 21 FM hydrodynamic (HD) and transport (TR) model was used to assess salinity transport during a neap–spring tidal cycle. The 2D model was calibrated and validated for the 2008 and 2011 flow events using field data on water levels and salinity. Results show an increase in tidal amplitude landward under low river flow conditions, while tidal damping was observed under higher river discharge, reducing the BRE salinity during spring tides. This study found that salinity intrusion is highly sensitive to freshwater availability, with river discharges of 150 m³/s and 175 m³/s identified as critical thresholds to maintain salinity levels below 1 PSU at the estuary mouth during ebb and flood tides, respectively. Flushing time analysis indicates that the BRE takes approximately 302 h to completely displace saline water when the river discharge is optimized at 150 m³/s. Modifying the BRE estuary mouth—through channel widening or deepening—enhanced the flushing process, significantly reducing salinity levels. This study demonstrates that optimizing freshwater discharge and modifying the estuary mouth can provide effective solutions for managing salinity intrusion in micro-tidal estuaries.

Blockage of culverts causes reduction in hydraulic capacity and is one of the main contributors to trigger urban flooding. However, the highly non-linear nature of debris interaction during the flood and lack of blockage-related data from actual flooding events make conventional numerical modelling almost impossible. Literature investigating blockage phenomena reports blockage as a complex hydraulic process, which suggests exploring adaptive solutions using latest technologies. In this context, motivated by the success of data-driven algorithms, in this article, four data driven models (i.e., K-NN, ANN, SVR, 1D-CNN) are implemented to predict the hydraulic blockage at culverts. A new numerical Hydraulics-Lab Blockage Dataset (HBD) is established from a series of lab-scale hydraulic experiments. From the experimental investigations, the ANN model was reported as the best with a R₂ score of 0.95. A potential use-case of presented research for real-world application is also discussed to demonstrate the practical feasibility.