SERVIR-Africa: Developing an Integrated Platform for Floods Disaster Management in Africa

SERVIR-Africa is an ambitious regional visualization and monitoring system that integrates remotely sensed data with predictive models and field-based data to monitor ecological processes and respond to natural disasters. It aims addressing societal benefits including floods and turning data into actionable information for decision-makers. Floods are exogenous disasters that affect many parts of Africa, probably second only to drought in terms of social-economic losses. This paper looks at SERVIR-Africa's approach to floods disaster management through establishment of an integrated platform, floods prediction models, post-event flood mapping and monitoring as well as flood maps dissemination in support of flood disaster management

Improving data driven decision making through integration of environmental sensing technologies

Coastal and estuarine zones contain vital and increasingly exploited resources. Traditional uses in these areas (transport, fishing, tourism) now sit alongside more recent activities (mineral extraction, wind farms). However, protecting the resource base upon which these marine-related economic and social activities depend requires access to reliable and timely data. This requires both acquisition of background (baseline) data and monitoring impacts of resource exploitation on aquatic processes and the environment. Management decisions must be based on analysis of collected data to reduce negative impacts while supporting resource-efficient, environmentally sustainable uses. Multi-modal sensing and data fusion offer attractive possibilities for providing such data in a resource efficient and robust manner. In this paper, we report the results of integrating multiple sensing technologies, including autonomous multi-parameter aquatic sensors with visual sensing systems. By focussing on salinity measurements, water level and freshwater influx into an estuarine system; we demonstrate the potential of modelling and data mining techniques in allowing deployment of fewer sensors, with greater network robustness. Using the estuary of the River Liffey in Dublin, Ireland, as an example, we present the outputs and benefits resulting from fusion of multi-modal sensing technologies to predict and understand freshwater input into estuarine systems and discuss the potential of multi-modal datasets for informed management decisions

A modified flood severity assessment for enhanced decision support: application to the Boscastle flash flood of 2004

A modified flash flood severity assessment is presented, based on scoring a set of factors according to their potential for generating extreme catchment-scale flooding. Improvements are made to the index through incorporation of parameter uncertainties, managing data absence, and clearer graphical communication. The motive for proposing these changes is to better inform flood managers during the development of a flash flood that may require an emergency response. This modified decision-support system is demonstrated for the Boscastle flood of 2004 and other historical floods in the United Kingdom. For Boscastle, the extreme nature of the flood is underestimated, which is likely to be due to the lack of sophistication in weighting flood parameters. However, the proposed amendments are able to rapidly reflect the reliability of a catchment severity rating, which may further enhance this technique as a decision-support tool alongside radar observations of localized storms

Distributed localized contextual event reasoning under uncertainty

We focus on Internet of Things (IoT) environments where sensing and computing devices (nodes) are responsible to observe, reason, report and react to a specific phenomenon. Each node captures context from data streams and reasons on the presence of an event. We propose a distributed predictive analytics scheme for localized context reasoning under uncertainty. Such reasoning is achieved through a contextualized, knowledge-driven clustering process, where the clusters of nodes are formed according to their belief on the presence of the phenomenon. Each cluster enhances its localized opinion about the presence of an event through consensus realized under the principles of Fuzzy Logic (FL). The proposed FLdriven consensus process is further enhanced with semantics adopting Type-2 Fuzzy Sets to handle the uncertainty related to the identification of an event. We provide a comprehensive experimental evaluation and comparison assessment with other schemes over real data and report on the benefits stemmed from its adoption in IoT environments

ReAFFIRM: Real-time Assessment of Flash Flood Impacts: a Regional high-resolution Method

Flash floods evolve rapidly in time, which poses particular challenges to emergency managers. One way to support decision-making is to complement models that estimate the flash flood hazard (e.g. discharge or return period) with tools that directly translate the hazard into the expected socio-economic impacts. This paper presents a method named ReAFFIRM that uses gridded rainfall estimates to assess in real time the flash flood hazard and translate it into the corresponding impacts. In contrast to other studies that mainly focus on in- dividual river catchments, the approach allows for monitoring entire regions at high resolution. The method consists of the following three components: (i) an already existing hazard module that processes the rainfall into values of exceeded return period in the drainage network, (ii) a flood map module that employs the flood maps created within the EU Floods Directive to convert the return periods into the expected flooded areas and flood depths, and (iii) an impact assessment module that combines the flood depths with several layers of socio- economic exposure and vulnerability. Impacts are estimated in three quantitative categories: population in the flooded area, economic losses, and affected critical infrastructures. The performance of ReAFFIRM is shown by applying it in the region of Catalonia (NE Spain) for three significant flash flood events. The results show that the method is capable of identifying areas where the flash floods caused the highest impacts, while some locations affected by less significant impacts were missed. In the locations where the flood extent corresponded to flood observations, the assessments of the population in the flooded area and affected critical infrastructures seemed to perform reasonably well, whereas the economic losses were systematically overestimated. The effects of different sources of uncertainty have been discussed: from the estimation of the hazard to its translation into impacts, which highly depends on the quality of the employed datasets, and in particular on the quality of the rainfall inputs and the comprehensiveness of the flood maps.Peer ReviewedPostprint (published version

Surface water flood warnings in England: overview, Assessment and recommendations based on survey responses and workshops

Following extensive surface water flooding (SWF) in England in summer 2007, progress has been made in improving the management and prediction of this type of flooding. A rainfall threshold-based extreme rainfall alert (ERA) service was launched in 2009 and superseded in 2011 by the surface water flood risk assessment (SWFRA). Through survey responses from local authorities (LAs) and the outcome of workshops with a range of flood professionals, this paper examines the understanding, benefits, limitations and ways to improve the current SWF warning service. The current SWFRA alerts are perceived as useful by district and county LAs, although their understanding of them is limited. The majority of LAs take action upon receipt of SWFRA alerts, and their reactiveness to alerts appears to have increased over the years and as SWFRA superseded ERA. This is a positive development towards increased resilience to SWF. The main drawback of the current service is its broad spatial resolution. Alternatives for providing localised SWF forecast and warnings were analysed, and a two-tier national-local approach, with pre-simulated scenario-based local SWF forecasting and warning systems, was deemed most appropriate by flood professionals given current monetary, human and technological resources

위성영상 융합과 의사결정 나무를 이용한 홍수 침범지역 지도화

학위논문 (석사)-- 서울대학교 대학원 : 농업생명과학대학 생태조경·지역시스템공학부, 2018. 2. Dong Kun Lee.The mapping of spatial inundation patterns during flood events is important for environmental management and disaster monitoring. Satellite images provide important data sources for monitoring flood disasters. However, the trade-off between spatial and temporal resolutions of current satellite sensors limits their uses in flooding studies. This study applied data fusion models, the flexible spatiotemporal method, in generating synthetic flooding images with improved temporal and spatial resolution for flood mapping. This paper performs a detailed comparison of flood maps derived from for number of post-disaster prediction based on images acquired after the flooding, selected flood events in 2016 Tumen river in China. The result shows that the Landsat-like images generated can be successfully applied in flood mapping. From simulated Tumen river flood mapping during 29 August to 3 September,2016, can know when inundation occurs, this result map flood inundation region will full in map. Meanwhile, test the maximum inundation region and severely submerged spots and flood event occur and stop date during the event. The study suggests great potential of FSDAF in flooding research. Blending multi-sources images could also support other disaster studies that require remotely sensed data with both high spatial and temporal resolution.Chapter 1. Introduction 1 1.1. Study Background 1 1.2. Purpose of Research 3 Chapter 2. Literature Review 4 2.1. Image fusion method 4 2.1.1 Weighted function based 4 2.1.2 Unmixing based 5 2.1.3 Dictionary-pair learning based 6 2.1.4 Flexible Spatiotemporal Data Fusion (FSDAF) 7 2.2 Flood mapping 9 2.2.1 Pixel based classification 9 2.2.2 Object-based classification 10 2.2.3 Decision Tree 12 Chapter 3. Materials and Methods 14 3.1. Study Area 14 3.2 Material and Methods 15 3.2.1 Satellite Images and Data Processing 15 3.2.1.1 Landsat-8 Operational Land Imager (OLI) 17 3.2.1.2 Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4, Collection 18 3.2.2 Predicting flooding images through FSDAF and accuracy assessment 19 3.2.3 Flood mapping and accuracy assessment 23 3.2.4 Tumen river flood inundation simulation 28 Chapter 4. Result and Discussions 30 4.1. Comparison of predicted image and original Landsat image land cover type change 30 4.1.1 Test with satellite image in heterogeneous Landscape 30 4.1.2 Predicted surface reflectance on flood date 32 4.2 Flood inundation mapping 38 4.3 Tumen river flood event simulation 42 Chapter 5 Conclusions 44 Bibliography 48 Abstract (Korean) 56Maste

Architecture of Environmental Risk Modelling: for a faster and more robust response to natural disasters

Demands on the disaster response capacity of the European Union are likely to increase, as the impacts of disasters continue to grow both in size and frequency. This has resulted in intensive research on issues concerning spatially-explicit information and modelling and their multiple sources of uncertainty. Geospatial support is one of the forms of assistance frequently required by emergency response centres along with hazard forecast and event management assessment. Robust modelling of natural hazards requires dynamic simulations under an array of multiple inputs from different sources. Uncertainty is associated with meteorological forecast and calibration of the model parameters. Software uncertainty also derives from the data transformation models (D-TM) needed for predicting hazard behaviour and its consequences. On the other hand, social contributions have recently been recognized as valuable in raw-data collection and mapping efforts traditionally dominated by professional organizations. Here an architecture overview is proposed for adaptive and robust modelling of natural hazards, following the Semantic Array Programming paradigm to also include the distributed array of social contributors called Citizen Sensor in a semantically-enhanced strategy for D-TM modelling. The modelling architecture proposes a multicriteria approach for assessing the array of potential impacts with qualitative rapid assessment methods based on a Partial Open Loop Feedback Control (POLFC) schema and complementing more traditional and accurate a-posteriori assessment. We discuss the computational aspect of environmental risk modelling using array-based parallel paradigms on High Performance Computing (HPC) platforms, in order for the implications of urgency to be introduced into the systems (Urgent-HPC).Comment: 12 pages, 1 figure, 1 text box, presented at the 3rd Conference of Computational Interdisciplinary Sciences (CCIS 2014), Asuncion, Paragua