Using sensor web technologies to help predict and monitor floods in urban areas

Includes abstract.Includes bibliographical references.Since flooding is worldwide one of the most common natural disasters, a number of flood prediction and monitoring approaches have been used. A lot of research has been conducted on the prediction and monitoring of floods by using hydrological models. The problem is that current hydrological models do not offer Disaster Management officials or township residents with timely data and information. In South Africa, possible flood warnings are usually communicated by Disaster Management officials using traditional approaches such as loudspeakers, radio and Television (TV). Making calls to warn residents about the possible occurrence of floods by using such means are, however, neither sufficient nor effective. As the result of improved communication, sensor, software and computing capabilities, the use of sensor networks and sensor web for predicting and monitoring environment have been considered in recent years. In order for sensor data such as sensor measurements, sensor descriptions and alerts to be integrated, the Open Geospatial Consortium (OGC) introduced the Sensor Web enablement (SWE) standards and suggested different specifications with respect to the geospatial sensor web. The first implementation of the sensor web framework is available. In this research, the results of using the sensor web technologies for predicting and monitoring floods in the urban areas are presented. The aim of this research project is to illustrate how the sensor web technology can help in the prediction and monitoring of floods in the urban areas, particularly in the Alexandra Township (Greater Johannesburg) which has experienced floods each and every year. The focus of this research is on the incorporation of the sensor data into the sensor web technology. The data used as input to sensor web and the hydrological model was historical rainfall data from the South African Weather Service (SAWS). Shuttle Radar Topography Mission (SRTM) free data from the internet was also used in this research

A smart sewer asset information model to enable an ‘Internet of Things’ for operational wastewater management

Real-time prediction of flooding is vital for the successful future operational management of the UK sewerage network. Recent advances in smart infrastructure and the emergence of the Internet of Things (IoT), presents an opportunity within the wastewater sector to harness and report in real-time sewer condition data for operation management. This study presents the design and development of a prototype Smart Sewer Asset Information Model (SSAIM) for an existing sewerage network. The SSAIM, developed using Industry Foundation Class version 4 (IFC4) an open neutral data format for BIM, incorporates distributed smart sensors to enable real-time monitoring and reporting of sewer asset performance. Results describe an approach for sensor data analysis to facilitate the real-time prediction of flooding

Web GIS to support irrigation management: a prototype for SAGRA network, Alentejo Portugal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAn efficient water management, not only allows significant savings in costs of irrigation, but also an effective control on the quality of products, which can have obvious consequences on income operation and reducing the environmental impact of irrigation. As the Internet is becoming the easiest way of information distribution, irrigation management system can also be benefitted with it. Integrating GIS functionality with internet capacity will redefine the way of decision making, sharing and processing of information. In irrigation systems weather plays an imperative role in decision making, implementing and forecasting. Temperature, humidity, precipitation, and solar radiation are the most important parameters to calculate evapotranspiration by which crop water requirement can be determined. SAGRA (Sistema Agrometeorológico para a Gestão da Rega no Alentejo) network is providing information to the farmers through web but still lacks the use of GIS in their information to decision support system. Irrigation management support system can be benefitted with the use of Web GIS. In this thesis, web based GIS is designed using popular open source tools and software. Using data from automatic weather station maps are produced using Geo-statistical interpolation techniques and published in web map. These maps can be viewed with popular online maps like Google maps, Microsoft Bing and Openstreet maps. Animated weather maps are also created which are useful for visualizing changing pattern of weather parameters and water requirement over time

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Sensor data and metadata standards review for UKCEH

This report contains a review and summary of a number of technical specifications originating with the ISO, OGC and W3C standards bodies in the field of earth observations and sensor networks, with the aim of developing a data model appropriate for CEH sensor data. In particular it recommends the development of a JSON-LD based format built around core concepts drawn jointly from ISO19156 Observations and Measurements and W3C Semantic Sensor Networks (SSN)/Sensor, Observation, Sample and Actuator (SOSA); the use of the Complex Property Model for semantically grounded property descriptions; OGC Sensor ML for the description of sensor instances and types; and INSPIRE Environmental Monitoring Facilities for describing sites and their monitoring capabilities. For time series representation it recommends the use of observation collections (a SSN/SOSA extension) which also serve as a point of attachment for property/values shared by all observations in a collection