scholarly journals The Swiss joint information platform for natural hazards

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Philipp Angehrn ◽  
Sabina Steiner ◽  
Christophe Lienert
Keyword(s):  
Natural Hazards ◽  
Spatial Data ◽  
Natural Hazard ◽  
Regional Scale ◽  
Measurement Data ◽  
User Feedback ◽  
Acceptance Testing ◽  
Spatial Density ◽  
Data Sets ◽  
Information Platform

<p><strong>Abstract.</strong> The Swiss Joint Information Platform for Natural Hazards (GIN) has been realized from 2008 to 2010 as part of the Swiss federal government’s OWARNA project, which aimed at optimizing warning and alerting procedures against natural hazard. The first online-version of the platform went productive in 2011 with the primary goal of providing measured and forecast natural hazard data in form of processed cartographic, graphic and other multimedia products to professional users &amp;ndash; before, during and after natural hazard events. In Switzerland water-, weather-, snow- and earthquake-related hazards are the most relevant ones.</p><p>In 2013, an online survey showed that the platform does not fully meet user expectations, particularly as to user experience and usability of its cartographic, web-based user interface. Revaluation and redesign of the overall platform were necessary in order to improve map legibility, caused by the complexity of data, large data amounts, and high spatial density of online, real-time measurement data locations. A new web design and user interaction concept have been developed in 2014 and eventually put online in June 2017. User acceptance testing by means of surveys and direct user feedback sessions were key factors in this perennial redesign process. The GIN platform now features important novel technical and graphical elements: The starting page is based on a dashboard containing virtual dossiers (Fig. 1), with which users configure their desired information, data, and map bundles individually, or use predefined adaptable views on various existing data sets. In addition, there is a new overall spatial search function to query data parameters. A responsive approach further improves the usability of the platform. The focus of these new features is on multi-views involving maps, diagrams, tables, text products, as well as selected geographical areas on maps, and fast data queries (Fig. 2). Current user feedback suggests that the new GIN platform design is well received, and that it is moving closer to its very goal: online monitoring and management of natural hazard events by enhanced usability, more targeted and higher personalization.</p><p>Several Swiss Cantons (i.e., the political entities in Switzerland below the federation) actively participated, and still participate, in the conceptual GIN platform development process through advisory board meetings and consultations. On the operational level, Cantons actively provide and contribute further natural hazard information and measurement data from their own natural hazard monitoring networks. These additional Cantonal regional-scale data sets help to fill spatial data gaps, where no Federal data is available. GIN thusly integrates natural hazard data from Federal and Cantonal levels (and partly even private level), which adds value to all stakeholders on various political levels involved in natural hazard management (Federal, Cantonal, Regional, Communal crisis committees). Stakeholders not only use GIN’s ample database and cartographic product portfolio to accomplish their early warning and crisis management tasks, but also benefit from seamless, secure and reliable IT-services, provided by the Swiss Federal Government. With the new GIN platform, Switzerland has a powerful, integrative, and comprehensive tool for monitoring and responding to natural hazard events.</p>

scholarly journals GEOINFORMATION APPROACH FOR COMPLEX ANALYSIS OF MULTIPLE NATURAL HAZARD

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 375-381 ◽  
Author(s):  
V. Nikolova ◽  
P. Zlateva
Keyword(s):  
Natural Hazards ◽  
Spatial Data ◽  
Complex Analysis ◽  
Natural Hazard ◽  
Public Awareness ◽  
Limiting Factors ◽  
Simultaneous Occurrence ◽  
Suggested Approach ◽  
Natural Components ◽  
Gis Environment

<p><strong>Abstract.</strong> Natural hazards are existence of natural components and processes, which create a situation that could negatively affect people, the economy and the environment. In this concern, they are associated with the probability of negative impacts and they are considered as limiting factors for people's lives and activities. Rising public awareness about natural hazards could improve the quality of life, save financial resources and even save lives. Methodological issues of complex analysis of multiple natural hazards in geographic information system (GIS) environment are presented in the current paper on the example of floods and landslide assessment. The complicated nature of natural hazards and the interrelations between natural components require a complex analysis of natural hazard factors and an integrated assessment taking into account all aspects of different hazards as well as the overall hazard resulting from a probable simultaneous occurrence of several adverse natural phenomena. A special attention is given to the data as one of the most important component of the analysis. Different data formats and particularities of spatial data interpretation in GIS environment are considered. Having regard the nature of the data and the phenomenon being evaluated, different GIS spatial analysis tools (fuzzy overlay, weighted sum, interpolation) are applied together with mathematical analyses. The results of the current research and suggested approach could support decision makers in territorial planning and risk management.</p>

scholarly journals A Cloud-Enabled Application Framework for Simulating Regional-Scale Impacts of Natural Hazards on the Built Environment

2020 ◽  
Vol 6 ◽  
Author(s):  
Gregory G. Deierlein ◽  
Frank McKenna ◽  
Adam Zsarnóczay ◽  
Tracy Kijewski-Correa ◽  
Ahsan Kareem ◽  
...  
Keyword(s):  
Natural Hazards ◽  
High Performance ◽  
Information Technologies ◽  
Computational Models ◽  
Natural Hazard ◽  
Regional Scale ◽  
Application Framework ◽  
Modular Architecture ◽  
Engineering Research ◽  
Effective Development

With the goal to facilitate evaluation and mitigation of the risks from natural hazards, the Natural Hazards Engineering Research Infrastructure’s Computational Modeling, and Simulation Center (NHERI SimCenter) is developing computational workflows for regional hazard simulations. These simulations enable research to combine detailed assessments of individual facilities with comprehensive regional-scale simulations of natural hazard effects. By integration of multi-fidelity and multi-resolution models to assess natural hazard impacts on buildings, infrastructure systems and other constructed facilities, the approach enables the engineering analysis of public policies and socio-economic impacts. Effective development of platforms for high-resolution regional simulations requires modular workflows that can integrate state-of-the-art models with information technologies and high-performance computing resources. In this paper, the modular architecture of the computational workflow models is described and illustrated through testbed applications to evaluate regional building damage under an earthquake and a hurricane scenario. Developed and disseminated as open-source software on the NHERI DesignSafe Cyberinfrastructure, the computational models and workflows are enabling multi-disciplinary collaboration on research to mitigate the effects of natural hazard disasters.

Managing Pipe Facilities With GIS: A User Perspective

2002 ◽  
Author(s):  
Gordon Gin ◽  
Stuart M. Thompson ◽  
David J. Stortini
Keyword(s):  
Spatial Data ◽  
Measurement Data ◽  
Mechanical Damage ◽  
Analysis Data ◽  
Document Management ◽  
Data Sets ◽  
Electronic Document Management ◽  
Inspection Data ◽  
Damage Susceptibility ◽  
Pipeline Failures

The Facilities Data Manager (FDM), XFIALS and DaVinci Projects use a unique combination of geo-spatial data base technology, electronic document management and CAD technology to graphically maintain pipeline facilities data. The same common ORACLE 8i™ database (called ORION) hosts cathodic protection measurements, in-line inspection measurements and pipeline risk analysis data. AM/FM/GIS is used to support pipeline risk applications. We will follow the processes involved with using pipeline facilities data and pipeline measurement data to quantitatively calculate the risk of pipeline failures. Pipeline measurement data sets in our database include: mechanical damage susceptibility, pipeline inspections, close interval surveys, hydrostatic test and in-line inspection data.

Real-time Approximation of Photometric Polygonal Lights

2020 ◽  
Vol 3 (1) ◽  
pp. 1-18
Author(s):  
Christian Luksch ◽  
Lukas Prost ◽  
Michael Wimmer
Keyword(s):  
Real Time ◽  
Specular Reflection ◽  
Near Field ◽  
Measurement Data ◽  
Data Sets ◽  
Photometric Measurement ◽  
Integration Technique ◽  
Time Approximation ◽  
Light Emitter ◽  
Selection Of

We present a real-time rendering technique for photometric polygonal lights. Our method uses a numerical integration technique based on a triangulation to calculate noise-free diffuse shading. We include a dynamic point in the triangulation that provides a continuous near-field illumination resembling the shape of the light emitter and its characteristics. We evaluate the accuracy of our approach with a diverse selection of photometric measurement data sets in a comprehensive benchmark framework. Furthermore, we provide an extension for specular reflection on surfaces with arbitrary roughness that facilitates the use of existing real-time shading techniques. Our technique is easy to integrate into real-time rendering systems and extends the range of possible applications with photometric area lights.

scholarly journals An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

2020 ◽  
Vol 12 (1) ◽  
pp. 580-597
Author(s):  
Mohamad Hamzeh ◽  
Farid Karimipour
Keyword(s):  
Spatial Data ◽  
Spatial Models ◽  
Model Performance ◽  
Essential Elements ◽  
Petroleum System ◽  
Petroleum Exploration ◽  
System Level ◽  
Data Sets ◽  
Petroleum Potential ◽  
Spatial Data Sets

AbstractAn inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

scholarly journals Mining Co-Location Patterns with Rare Events from Spatial Data Sets

2006 ◽  
Vol 10 (3) ◽  
pp. 239-260 ◽  
Author(s):  
Yan Huang ◽  
Jian Pei ◽  
Hui Xiong
Keyword(s):  
Spatial Data ◽  
Rare Events ◽  
Data Sets ◽  
Location Patterns ◽  
Spatial Data Sets
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