JellyNet: The convolutional neural network jellyfish bloom detector

Coastal industries face disruption on a global scale due to the threat of large blooms of jellyfish. They can decimate coastal fisheries and clog the water intake systems of desalination and nuclear power plants. This can lead to losses of revenue and power output. This paper presents JellyNet: a convolutional neural network (CNN) jellyfish bloom detection model trained on high resolution remote sensing imagery collected by unmanned aerial vehicles (UAVs). JellyNet provides the detection capability for an early (6–8 h) bloom warning system. 1539 images were collected from flights at 2 locations: Croabh Haven, UK and Pruth Bay, Canada. The training/test dataset was manually labelled, and split into two classes: ‘Bloom present’ and ‘No bloom present’. 500 × 500 pixel images were used to increase fine-grained pattern detection of the jellyfish blooms. Model testing was completed using a 75/25% training/test split with hyperparameters selected prior to model training using a held-out validation dataset. Transfer learning using VGG-16 architecture, and a jellyfish bloom specific binary classifier surpassed an accuracy of 90%. Test model performance peaked at 97.5% accuracy. This paper exhibits the first example of a high resolution, multi-sensor jellyfish bloom detection capability, with integrated robustness from two oceans to tackle real world detection challenges

Protection motivation theory: a proposed theoretical extension and moving beyond rationality-the case of flooding

Despite the significant financial and non-financial costs of household flooding, and the availability of products that can reduce the risk or impact of flooding, relatively few consumers choose to adopt these products. To help explain this, we combine the existing theoretical literature with evidence from 20 one-to-one discussions and three workshops with key stakeholders, as well as five round tables, to draw practical evidence of actual responses to flood risk. This analysis leads us to propose an extension to Protection Motivation Theory (PMT), which more accurately captures the decision-making process of consumers by highlighting the role of 'ownership appraisal'. We then assess the extent to which behavioral biases impact on this revised framework. By highlighting the interaction with an augmented model of PMT and behavioral biases, the paper sheds light on potential reasons behind the fact that consumers are unlikely to adopt property-level flood resilience measures and identifies strategies to increase flood protection. The Augmented PMT suggests that policymakers might focus on increasing the Ownership Appraisal element, both directly and by targeting the creation of more supportive social norms. The work presented here opens up a wide range of areas for future research in the field

Understanding the effects of Digital Elevation Model resolution in urban fluvial flood modelling

With the extensive use of 2D flood models, the resolution and quality of Digital Elevation Models (DEMs) have come under greater focus especially in urban hydrology. One of the major research areas, in this regard, is the effect of DEM resolution on flood modelling. This study first investigates the root causes of the impact of DEM resolution on urban fluvial flood modelling outputs using DEMs with grid resolutions ranging from 1m to 50m. The study then investigates how DEM resolution affects the definition and characterisation of the river channel and the consequences of this for the modelled results. For this purpose, a separate set of merged DEMs was generated where the river channel as defined by the 1m resolution DEM is merged with coarser resolution DEMs. Data obtained during the flood event caused by Storm Desmond (2015) in Cockermouth (Cumbria, UK) was used for this study. The HEC-RAS 2D model was used for all of the simulations. The benchmark model obtained with the 1m resolution DEM was calibrated using measured water levels at two locations within the rivers. Results show that there is a 30% increase in flood extent from 58.9 ha to 79.0 ha and a 150% increase in mean flood depth from 1.74m to 4.30m when the resolution reduces from a 1m grid to a 50m grid. The main reason for this is the increasing lack of definition of the river channel with an associated reduction in the estimated depth of the river resulting in reduced river channel conveyance. This then leads to an increase in the flood extent and depth especially in the immediate vicinity of the river. This effect is amplified when the DEM grid size is greater than the river width. When the 1m resolution DEM for the river channel is used in conjunction with coarser resolution DEMs for the surrounding areas (merged DEMs), there is a significant improvement in the agreement between the modelled and the reference case (obtained from the benchmark model) flood extents and depths. The use of merged DEMs reduces the error in mean flood depth from 90% to 4% and reduces the overall RMSE in flood depths from 2.6m to 0.9m at 30m resolution. The 30m resolution DEM was tested because this is. The use of merged DEMs, where a higher resolution DEM is used to characterise the river channel in conjunction with a 30m resolution DEM (e.g., NASA Shuttle Radar Topography Mission DEMs) for the wider area could be a cost-effective solution for locations where higher resolution DEMs may not be available

Detection of flood damage in urban residential areas using object-oriented UAV image analysis coupled with tree-based classifiers

Timely clearing-up interventions are essential for effective recovery of flood-damaged housing, however, time-consuming door-to-door inspections for insurance purposes need to take place before major repairs can be done to adequately assess the losses caused by flooding. With the increased probability of flooding, there is a heightened need for rapid flood damage assessment methods. High resolution imagery captured by unmanned aerial vehicles (UAVs) offers an opportunity for accelerating the time needed for inspections, either through visual interpretation or automated image classification. In this study, object-oriented image segmentation coupled with tree-based classifiers was implemented on a 10 cm resolution RGB orthoimage, captured over the English town of Cockermouth a week after a flood triggered by storm Desmond, to automatically detect debris associated with damages predominantly to residential housing. Random forests algorithm achieved a good level of overall accuracy of 74%, with debris being correctly classified at the rate of 58%, and performing well for small debris (67%) and skips (64%). The method was successful at depicting brightly-colored debris, however, was prone to misclassifications with brightly-colored vehicles. Consequently, in the current stage, the methodology could be used to facilitate visual interpretation of UAV images. Methods to improve accuracy have been identified and discussed

A mixed-methods investigation into barriers for sharing geospatial and resilience flood data in the UK

With increases in average temperature and rainfall predicted, more households are expected to be at risk of flooding in the UK by 2050. Data and technologies are increasingly playing a critical role across public-, private- and third-sector organisations. However, barriers and constraints exist across organisations and industries that limit the sharing of data. We examine the international context for data sharing and variations between data-rich and data-sparse countries. We find that local politics and organisational structures influence data sharing. We focus on the case study of the UK, and on geospatial and flood resilience data in particular. We use a series of semi-structured interviews to evaluate data sharing limitations, with particular reference to geospatial and flood resilience data. We identify barriers and constraints when sharing data between organisations. We find technological, security, privacy, cultural and commercial barriers across different use cases and data points. Finally, we provide three long-term recommendations to improve the overall accessibility to flood data and enhance outcomes for organisations and communities

Combining unmanned aircraft systems and image processing for wastewater treatment plant asset inspection

Wastewater treatment plants are essential for preserving the water quality of freshwater and marine ecosystems. It is estimated that, in the UK, as much as 11 billion liters of wastewater are treated on a daily basis. Effective and efficient treatment of wastewater requires treatment plants to be maintained in good condition. Recent studies have highlighted the potential of unmanned aircraft systems (UASs) and image processing to be used in autonomous and automated monitoring systems. However, the combined use of UASs and image processing for wastewater treatment plant inspections has not yet been tested. This paper presents a novel image processing-UAS framework for the identification of failures in trickling filters and activated sludge facilities. The results show that the proposed framework has an accuracy of 95% in the detection of failures in activated sludge assets, with the accuracy ranging between 55% and 81% for trickling filters. These results are promising and they highlight the potential use of the technology for the inspection of wastewater treatment plant

Characterisation and control of the biosolids storage environment: Implications for E. coli dynamics

E. coli survival in biosolids storage may present a risk of non-compliance with guidelines designed to ensure a quality product safe for agricultural use. The storage environment may affect E. coli survival but presently, storage characteristics are not well profiled. Typically biosolids storage environments are not actively controlled or monitored to support increased product quality or improved microbial compliance. This two-phased study aimed to identify the environmental factors that control bacterial concentrations through a long term, controlled monitoring study (phase 1) and a field-scale demonstration trial modifying precursors to bacterial growth (phase 2). Digested and dewatered biosolids were stored in operational-scale stockpiles to elucidate factors controlling E. coli dynamics. E. coli concentrations, stockpile dry solids, temperature, redox and ambient weather data were monitored. Results from ANCOVA analysis showed statistically significant (p 25 °C for a 28 day period and achieved a 3.7 Log E. coli reduction. In winter months E. coli suppression was limited with concentrations >6 Log10 CFU g−1 DS maintained. The ANCOVA analysis has identified the significant role that physical environmental factors, such as stockpile temperature, has on E. coli dynamics and the opportunities for contro

Association Between Preexisting Versus Newly Identified Atrial Fibrillation and Outcomes of Patients With Acute Pulmonary Embolism

Background Atrial fibrillation (AF) may exist before or occur early in the course of pulmonary embolism (PE). We determined the PE outcomes based on the presence and timing of AF. Methods and Results Using the data from a multicenter PE registry, we identified 3 groups: (1) those with preexisting AF, (2) patients with new AF within 2 days from acute PE (incident AF), and (3) patients without AF. We assessed the 90-day and 1-year risk of mortality and stroke in patients with AF, compared with those without AF (reference group). Among 16 497 patients with PE, 792 had preexisting AF. These patients had increased odds of 90-day all-cause (odds ratio [OR], 2.81; 95% CI, 2.33-3.38) and PE-related mortality (OR, 2.38; 95% CI, 1.37-4.14) and increased 1-year hazard for ischemic stroke (hazard ratio, 5.48; 95% CI, 3.10-9.69) compared with those without AF. After multivariable adjustment, preexisting AF was associated with significantly increased odds of all-cause mortality (OR, 1.91; 95% CI, 1.57-2.32) but not PE-related mortality (OR, 1.50; 95% CI, 0.85-2.66). Among 16 497 patients with PE, 445 developed new incident AF within 2 days of acute PE. Incident AF was associated with increased odds of 90-day all-cause (OR, 2.28; 95% CI, 1.75-2.97) and PE-related (OR, 3.64; 95% CI, 2.01-6.59) mortality but not stroke. Findings were similar in multivariable analyses. Conclusions In patients with acute symptomatic PE, both preexisting AF and incident AF predict adverse clinical outcomes. The type of adverse outcomes may differ depending on the timing of AF onset.info:eu-repo/semantics/publishedVersio

Accuracy Assessment of Surveying Strategies for the Characterization of Microtopographic Features That Influence Surface Water Flooding

With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, particularly for the shallow flow associated with urban SWF. This study compares two survey strategies commonly used by flood practitioners, S1 (using Unmanned Aerial Systems-based RGB data) and S2 (using manned aircraft with LiDAR scanners), to develop guidelines on where to use each strategy to better characterise microtopography for a range of flood features. The difference between S1 and S2 in elevation and their accuracies were assessed using both traditional and robust statistical measures. The results showed that the difference in elevation between S1 and S2 varies between 11 cm and 37 cm on different land use and microtopographic flood features. Similarly, the accuracy of S1 ranges between 3 cm and 70 cm, and the accuracy of S2 ranges between 3.8 cm and 30.3 cm on different microtopographic flood features. Thus, this study suggests that the flood features of interest in any given flood study would be key to select the most suitable survey strategy. A decision framework was developed to inform data collection and integration of the two surveying strategies to better characterise microtopographic features. The findings from this study will help improve the microtopographic representation of flood features in flood models and, thus, increase the ability to identify high flood-risk prompt areas accurately. It would also help manage and maintain drainage assets, spatial planning of sustainable drainage systems, and property level flood resilience and insurance to better adapt to the effects of climate change. This study is another step towards standardising flood extent and impact surveying strategies