Remote sensing of intertidal morphological change in Morecambe Bay, U.K., between 1991 and 2007

Tidal Flats are important examples of extensive areas of natural environment that remain relatively unaffected by man. Monitoring of tidal flats is required for a variety of purposes. Remote sensing has become an established technique for the measurement of topography over tidal flats. A further requirement is to measure topographic changes in order to measure sediment budgets. To date there have been few attempts to make quantitative estimates of morphological change over tidal flat areas. This paper illustrates the use of remote sensing to measure quantitative and qualitative changes in the tidal flats of Morecambe Bay during the relatively long period 1991–2007. An understanding of the patterns of sediment transport within the Bay is of considerable interest for coastal management and defence purposes. Tidal asymmetry is considered to be the dominant cause of morphological change in the Bay, with the higher currents associated with the flood tide being the main agency moulding the channel system. Quantitative changes were measured by comparing a Digital Elevation Model (DEM) of the intertidal zone formed using the waterline technique applied to satellite Synthetic Aperture Radar (SAR) images from 1991–1994, to a second DEM constructed from airborne laser altimetry data acquired in 2005. Qualitative changes were studied using additional SAR images acquired since 2003. A significant movement of sediment from below Mean Sea Level (MSL) to above MSL was detected by comparing the two Digital Elevation Models, though the proportion of this change that could be ascribed to seasonal effects was not clear. Between 1991 and 2004 there was a migration of the Ulverston channel of the river Leven north-east by about 5 km, followed by the development of a straighter channel to the west, leaving the previous channel decoupled from the river. This is thought to be due to independent tidal and fluvial forcing mechanisms acting on the channel. The results demonstrate the effectiveness of remote sensing for measurement of long-term morphological change in tidal flat areas. An alternative use of waterlines as partial bathymetry for assimilation into a morphodynamic model of the coastal zone is also discussed

A deployed multi agent system for meteorological alerts

The Australian Bureau of Meteorology has a requirement for complex and evolving systems to manage its weather forecasting, monitoring and alerts. This paper describes a system that monitors in real time the current terminal area forecasts (forecasts for areas around airports) and alerts forecasters to inconsistencies between these and observations obtained from automatic weather station (AWS) data. The contributions of the paper are a description of the overall architecture including legacy components, and the mechanisms that have been used to interface to legacy components; a description of an inferencing mechanism, available in recent versions of the JACK Intelligent Agents toolkit which has been particularly useful in some of the reasoning needed in this application; and a detailed description of the architecture for data sharing and data management. The system is currently deployed and a project is underway to extend this to a much larger system

London Battersea Heliport Noise Monitoring

London South Bank University Enterprise Ltd was contracted by Wandsworth Council to undertaken noise monitoring around three boroughs surrounding Battersea Heliport. The Heliport consultative committee provided a list of volunteers which was used to select the dwellings used in the monitoring. Monitoring was undertaken over the spring/summer of 2017 to establish baseline noise levels for the residents both internally and externally. Measurements were taken during heliport operating hours: 0700-2300. Long terms measurements were taken at four locations in three boroughs and these were compared to the latest noise criteria in English planning guidance, ProPG: Planning and Noise 2017, British Standards BS8233:2014, Aviation Framework Policy 2013 and to the local planning condition set by the Greater London Council. It was found that the noise environment along the heliport flight path was at levels which would cause significant adverse impact, a medium level of annoyance, and run a low to medium risk of causing long term adverse health effects. The local planning condition was regularly exceeded along the flight path. Well away from the heliport and its associated flight path the noise environment was broadly compliant with guidance, standards and policy

Novel sound absorption materials produced from air laid non-woven feather fibres

This research has investigated the use of feather fibres to produce sound absorption materials as an alternative to the oil derived synthetic plastics that currently dominate the sound absorption materials market. In this paper we show that clean and disinfected waste feathers from the poultry industry can be processed into fibres and air laid using commercial pilot plant facilities to form non-woven feather fibre composite mats. By varying the composition and processing conditions, materials with a range of different properties such as thickness and density were produced. The sound absorption coefficients of samples was determined using the impedance tube method (BS EN ISO 10534-2: 1998), using normal incidence sound between 80 and 1,600 Hz. The data reported shows that air laid non-woven feather fibre mats have improved sound absorption coefficients compared to other natural materials used for sound absorption for a given thickness, particularly in the problematic low frequency range between 250 to 800 Hz. We conclude that air laid non-woven feather fibres have high potential to be used as effective and sustainable sound absorption materials in aerospace, automotive, buildings, infrastructure and other applications where sound absorption is required

Assessing the spatial spread–skill of ensemble flood maps with remote-sensing observations

An ensemble of forecast flood inundation maps has the potential to represent the uncertainty in the flood forecast and provide a location-specific likelihood of flooding. Ensemble flood map forecasts provide probabilistic information to flood forecasters, flood risk managers and insurers and will ultimately benefit people living in flood-prone areas. Spatial verification of the ensemble flood map forecast against remotely observed flooding is important to understand both the skill of the ensemble forecast and the uncertainty represented in the variation or spread of the individual ensemble-member flood maps. In atmospheric sciences, a scale-selective approach has been used to evaluate a convective precipitation ensemble forecast. This determines a skilful scale (agreement scale) of ensemble performance by locally computing a skill metric across a range of length scales. By extending this approach through a new application, we evaluate the spatial predictability and the spatial spread–skill of an ensemble flood forecast across a domain of interest. The spatial spread–skill method computes an agreement scale at every grid cell between each unique pair of ensemble flood maps (ensemble spatial spread) and between each ensemble flood map with a SAR-derived flood map (ensemble spatial skill; SAR: synthetic aperture radar). These two are compared to produce the final spatial spread–skill performance. These methods are applied to the August 2017 flood event on the Brahmaputra River in the Assam region of India. Both the spatial skill and spread–skill relationship vary with location and can be linked to the physical characteristics of the flooding event such as the location of heavy precipitation. During monitoring of flood inundation accuracy in operational forecasting systems, validation and mapping of the spatial spread–skill relationship would allow better quantification of forecast systematic biases and uncertainties. This would be particularly useful for ungauged catchments where forecast streamflows are uncalibrated and would enable targeted model improvements to be made across different parts of the forecast chain.</p

Deep learning for automated river-level monitoring through river camera images: an approach based on water segmentation and transfer learning

River level estimation is a critical task required for the understanding of flood events, and is often complicated by the scarcity of available data. Recent studies have proposed to take advantage of large networks of river camera images to estimate the river levels, but currently, the utility of this approach remains limited as it requires a large amount of manual intervention (ground topographic surveys and water image annotation). We develop an approach using an automated water semantic segmentation method to ease the process of river level estimation from river camera images. Our method is based on the application of a transfer learning methodology to deep semantic neural networks designed for water segmentation. Using datasets of image series extracted from four river cameras and manually annotated for the observation of a flood event on the Severn and Avon rivers, UK (21 November - 5 December 2012), we show that this algorithm is able to automate the annotation process with an accuracy greater than 91%. Then, we apply our approach to year-long image series from the same cameras observing the Severn and Avon (from 1 June 2019 to 31 May 2020) and compare the results with nearby river-gauge measurements. Given the high correlation (Pearson's Correlation Coefficient >0.94) between these results and the river-gauge measurements, it is clear that our approach to automation of the water segmentation on river camera images could allow for straightforward, inexpensive observation of flood events, especially at ungauged locations

Noise reduction in commercial refrigerators - a practical approach

An Adande refrigeration unit originally designed for use in the commercial catering industry was redesigned for use in households. This sector is more sensitive to refrigeration noise, following the introduction of the EU noise labelling directive. A practical noise control ap-proach was taken consisting of benchmarking the existing commercial unit, diagnosing the primary noise sources, redesigning the system components without affecting the refrigera-tion performance and assessing improvements. The aim was to reduce noise emissions and improve sound quality to those of frost free household refrigerators. Value engineering was used to optimise the performance gains such that the new unit suitable for the domestic mar-ket would be also used in the commercial sector. The sound power reduction achieved was greater than 4 dB. The sound quality of both the existing standard refrigerator and the opti-mised prototype unit were evaluated by a jury in a real living environment. The subjective exercise showed that the optimised prototype was perceived as being quieter and of im-proved sound quality compared to the standard refrigerator