Conferences related to Disaster Prediction

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2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

All fields of satellite, airborne and ground remote sensing.


OCEANS 2020 - SINGAPORE

An OCEANS conference is a major forum for scientists, engineers, and end-users throughout the world to present and discuss the latest research results, ideas, developments, and applications in all areas of oceanic science and engineering. Each conference has a specific theme chosen by the conference technical program committee. All papers presented at the conference are subsequently archived in the IEEE Xplore online database. The OCEANS conference comprises a scientific program with oral and poster presentations, and a state of the art exhibition in the field of ocean engineering and marine technology. In addition, each conference can have tutorials, workshops, panel discussions, technical tours, awards ceremonies, receptions, and other professional and social activities.

  • OCEANS 2019 - Marseille

    Research, Development, and Operations pertaining to the Oceans

  • 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO)

    The conference scope is to provide a thematic umbrella for researchers working in OCEAN engineering and related fields across the world to discuss the problems and potential long term solutions that concernnot only the oceans in Asian pacific region, but the world ocean in general.

  • OCEANS 2017 - Aberdeen

    Papers on ocean technology, exhibits from ocean equipment and service suppliers, student posters and student poster competition, tutorials on ocean technology, workshops and town hall meetings on policy and governmental process.

  • OCEANS 2016 - Shanghai

    Papers on ocean technology, exhibits from ocean equipment and service suppliers, student posters and student poster competition, tutorial on ocean technology, workshops and town hall meetings on policy and governmental process.

  • OCEANS 2015 - Genova

    The Marine Technology Society and the Oceanic Engineering Society of IEEE cosponsor a joint annual conference and exposition on ocean science, engineering and policy. The OCEANS conference covers four days. One day for tutorials and three for approx. 450 technical papers and 50-200 exhibits.

  • OCEANS 2014 - TAIPEI

    The OCEANS conference covers all aspects of ocean engineering from physics aspects through development and operation of undersea vehicles and equipment.

  • OCEANS 2013 - NORWAY

    Ocean related technologies. Program includes tutorials, three days of technical papers and a concurrent exhibition. Student poster competition.

  • OCEANS 2012 - YEOSU

    The OCEANS conferences covers four days with tutorials, exhibits and three days of parallel tracks that address all aspects of oceanic engineering.

  • OCEANS 2011 - SPAIN

    All Oceans related technologies.

  • OCEANS 2010 IEEE - Sydney

  • OCEANS 2009 - EUROPE

  • OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean

  • OCEANS 2007 - EUROPE

    The theme 'Marine Challenges: Coastline to Deep Sea' focuses on the significant challenges, from the shallowest waters around our coasts to the deepest subsea trenches, that face marine, subsea and oceanic engineers in their drive to understand the complexities of the world's oceans.

  • OCEANS 2006 - ASIA PACIFIC

  • OCEANS 2005 - EUROPE


Oceans 2020 MTS/IEEE GULF COAST

To promote awareness, understanding, advancement and application of ocean engineering and marine technology. This includes all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.

  • OCEANS 2021 San Diego

    Covering Oceanography as a whole - instrumentation, science, research, biology, subsea and surface vehicles, autonomous vehicles, AUV, ROV, manned submersibles, global climate, oceanography, oceanology, rivers, estuaries, aquatic life and biology, water purity, water treatment, sonar, mapping, charting, navigation, navigation safety, oil and gas, military, and commercial applications of the oceans, subsea mining, hot vents, adn more.

  • OCEANS 2018 MTS/IEEE Charleston

    Ocean, coastal, and atmospheric science and technology advances and applications

  • OCEANS 2017 - Anchorage

    Papers on ocean technology, exhibits from ocean equipment and service suppliers, student posters and student poster competition, tutorials on ocean technology, workshops and town meetings on policy and governmental process.

  • OCEANS 2016

    The Marine Technology Scociety and the Oceanic Engineering Society of the IEEE cosponor a joint annual conference and exposition on ocean science, engineering, and policy. The OCEANS conference covers four days. One day for tutorials and three for approx. 500 technical papers and 150 -200 exhibits.

  • OCEANS 2015

    The Marine Technology Scociety and the Oceanic Engineering Society of the IEEE cosponor a joint annual conference and exposition on ocean science, engineering, and policy. The OCEANS conference covers four days. One day for tutorials and three for approx. 450 technical papers and 150-200 exhibits.

  • OCEANS 2014

    The OCEANS conference covers four days. One day for tutorials and three for approx. 450 technical papers and 150-200 exhibits.

  • OCEANS 2013

    Three days of 8-10 tracks of technical sessions (400-450 papers) and concurent exhibition (150-250 exhibitors)

  • OCEANS 2012

    Ocean related technology. Tutorials and three days of technical sessions and exhibits. 8-12 parallel technical tracks.

  • OCEANS 2011

    The Marine Technology Society and the Oceanic Engineering Scociety of the IEEE cosponsor a joint annual conference and exposition on ocean science engineering, and policy.

  • OCEANS 2010

    The Marine Technology Society and the Oceanic Engineering Scociety of the IEEE cosponsor a joint annual conference and exposition on ocean science engineering, and policy.

  • OCEANS 2009

  • OCEANS 2008

    The Marine Technology Society (MTS) and the Oceanic Engineering Society (OES) of the Institute of Electrical and Electronic Engineers (IEEE) cosponsor a joint conference and exposition on ocean science, engineering, education, and policy. Held annually in the fall, it has become a focal point for the ocean and marine community to meet, learn, and exhibit products and services. The conference includes technical sessions, workshops, student poster sessions, job fairs, tutorials and a large exhibit.

  • OCEANS 2007

  • OCEANS 2006

  • OCEANS 2005

  • OCEANS 2004

  • OCEANS 2003

  • OCEANS 2002

  • OCEANS 2001

  • OCEANS 2000

  • OCEANS '99

  • OCEANS '98

  • OCEANS '97

  • OCEANS '96


2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

Cluster Computing, Grid Computing, Edge Computing, Cloud Computing, Parallel Computing, Distributed Computing


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Periodicals related to Disaster Prediction

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Applied Superconductivity, IEEE Transactions on

Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission


Communications Magazine, IEEE

IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...


Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on

Methods, algorithms, and human-machine interfaces for physical and logical design, including: planning, synthesis, partitioning, modeling, simulation, layout, verification, testing, and documentation of integrated-circuit and systems designs of all complexities. Practical applications of aids resulting in producible analog, digital, optical, or microwave integrated circuits are emphasized.


Engineering Management, IEEE Transactions on

Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.


Geoscience and Remote Sensing Letters, IEEE

It is expected that GRS Letters will apply to a wide range of remote sensing activities looking to publish shorter, high-impact papers. Topics covered will remain within the IEEE Geoscience and Remote Sensing Societys field of interest: the theory, concepts, and techniques of science and engineering as they apply to the sensing of the earth, oceans, atmosphere, and space; and ...


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Most published Xplore authors for Disaster Prediction

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Xplore Articles related to Disaster Prediction

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Study on the development of seismic disaster prediction of lifeline systems based on ESRI ArcGIS engine 9

2007 IEEE International Geoscience and Remote Sensing Symposium, 2007

The application of GIS techniques to the seismic damage and loss prediction of lifelines is introduced in the paper. The basic models to predict and the main function of the system are demonstrated. Some user interfaces are depicted as examples to show GIS solution instances.


RSNN-Based Instability Disaster Prediction of Tailings Dam

2009 International Conference on Computational Intelligence and Software Engineering, 2009

The instability disaster prediction model of tailings dam had been established, based on system analysis of the factors that caused the instability disaster of tailings dam, by selecting 6 prediction index, medium unit weight, cohesion, internal friction angle, slope angle, slope height and pore pressure ratio and combining with using theory of the rough set and neural network. First the ...


Notice of Retraction<br>Key technology of disaster prediction and intelligent decision-making system

2010 3rd International Conference on Computer Science and Information Technology, 2010

How to deign and develop an disaster prediction and intelligent decision- making system of disaster is a hot topic now. Analyses the characteristics of prediction and early warning method of mining engineering disaster, a structure of a technology framework based on spatial agent is presented, and described the structure of a spatial agent, the integrated behaviors of the spatial agent ...


Disaster-prediction based virtual network mapping against multiple regional failures

2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), 2015

Survivable virtual network mapping (SVNM) has been extensively investigated to guarantee that the mapped virtual network (VN) works normally against substrate failures. The existing studies of SVNM mainly focus on single node or single link failure. Since natural disasters usually cause severe substrate failures in geographic regions, some work addressing SVNM against regional failures has been studied. However, the current ...


PREDICAT: A Semantic Service-Oriented Platform for Data Interoperability and Linking in Earth Observation and Disaster Prediction

2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA), 2018

The increasing volume of data generated by earth observation programs such as Copernicus, NOAA, and NASA Earth Data, is overwhelming. Although these programs are very costly, data usage remains limited due to lack of interoperability and data linking. In fact, multi-source and heterogeneous data exploitation could be significantly improved in different domains especially in the natural disaster prediction one. To ...


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Educational Resources on Disaster Prediction

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IEEE-USA E-Books

  • Study on the development of seismic disaster prediction of lifeline systems based on ESRI ArcGIS engine 9

    The application of GIS techniques to the seismic damage and loss prediction of lifelines is introduced in the paper. The basic models to predict and the main function of the system are demonstrated. Some user interfaces are depicted as examples to show GIS solution instances.

  • RSNN-Based Instability Disaster Prediction of Tailings Dam

    The instability disaster prediction model of tailings dam had been established, based on system analysis of the factors that caused the instability disaster of tailings dam, by selecting 6 prediction index, medium unit weight, cohesion, internal friction angle, slope angle, slope height and pore pressure ratio and combining with using theory of the rough set and neural network. First the rough set theory was used for the creation of decision table, data mining, attribution importance ranking and reducing, then the decision table processed by rough set theory the table was used as the input of the neural network and the algorithm of back propagation was used to train the prediction model. It was shown that the prediction values output by the model agrees well with the actual value and the accuracy of prediction was high. Research showed that the mathematics prediction method overcomes the bottleneck of neural network in slowing training efficiency and low prediction accuracy, providing an optimization method for risk prediction of tailings dam.

  • Notice of Retraction<br>Key technology of disaster prediction and intelligent decision-making system

    How to deign and develop an disaster prediction and intelligent decision- making system of disaster is a hot topic now. Analyses the characteristics of prediction and early warning method of mining engineering disaster, a structure of a technology framework based on spatial agent is presented, and described the structure of a spatial agent, the integrated behaviors of the spatial agent community in case study of a mine, which supports for disaster management and remote engineering control.

  • Disaster-prediction based virtual network mapping against multiple regional failures

    Survivable virtual network mapping (SVNM) has been extensively investigated to guarantee that the mapped virtual network (VN) works normally against substrate failures. The existing studies of SVNM mainly focus on single node or single link failure. Since natural disasters usually cause severe substrate failures in geographic regions, some work addressing SVNM against regional failures has been studied. However, the current approaches only solve the mapping problem against single regional failure. When there are multiple regional failures aroused by natural disasters, such approaches are not effective. In this paper, we first design a regional failure model with the knowledge of risk assessment. Then we propose two effective mapping algorithms based on the disaster-prediction scheme with the regional failure model. One is the minimum link risk prior selection algorithm and the other is the asymmetric parallel flow allocation algorithm. Simulation results show that both approaches can reduce the capacity loss of virtual networks caused by regional failures and can effectively increase the average VN acceptance ratio.

  • PREDICAT: A Semantic Service-Oriented Platform for Data Interoperability and Linking in Earth Observation and Disaster Prediction

    The increasing volume of data generated by earth observation programs such as Copernicus, NOAA, and NASA Earth Data, is overwhelming. Although these programs are very costly, data usage remains limited due to lack of interoperability and data linking. In fact, multi-source and heterogeneous data exploitation could be significantly improved in different domains especially in the natural disaster prediction one. To deal with this issue, we introduce the PREDICAT project that aims at providing a semantic service- oriented platform to PREDIct natural CATastrophes. The PREDICAT platform considers (1) data access based on web service technology; (2) ontology-based interoperability for the environmental monitoring domain; (3) data integration and linking via big data techniques; (4) a prediction approach based on semantic machine learning mechanisms. The focus in this paper is to provide an overview of the PREDICAT platform architecture. A scenario explaining the operation of the platform is presented based on data provided by our collaborators, including the international intergovernmental Sahara and Sahel Observatory (OSS).

  • Multivariate grey disaster prediction model

    Grey catastrophe prediction can predict the date of catastrophe, so that people can take precautions in advance to reduce losses. For example, the use of catastrophe forecasting methods can predict the occurrence of disasters such as droughts, floods, landslides, typhoons and coal mine accidents, which is conducive to the government, relevant departments and the public to take corresponding actions to reduce the adverse effects of disasters on the society. The catastrophe prediction model is mainly to study the catastrophe date sequence and to find its regularity, and through the establishment of GM (1,1) model to predict the date of the subsequent catastrophe. As we can see, the occurrence of catastrophe is caused by the influence of multiple factors. Therefore, it is not enough to take only one factor into consideration when forecasting, combining it with other factors is necessary. Multivariate grey prediction model MGM(1, m) can describe the variables from the dimension of system, and can better reflect the relationship between the variables in the system, so as to predict multiple variables. Therefore, the study combines multivariate grey prediction with catastrophe prediction to propose a novel multivariate grey disaster prediction model. The new model comprehensively considers the relationship between the catastrophic dates of variables in the catastrophic system, avoids the shortcomings of the traditional single factor grey disaster prediction and predicts the date of the next catastrophe from the dimension of system. Multivariate grey disaster prediction model is a combination of catastrophe prediction model and multivariate grey prediction model. First, find n catastrophe dates which belong to the first variable. Then, based on the n catastrophe dates of the first variable, find the n catastrophe dates of other variables respectively and get the catastrophe date matrix. Finally, the prediction can be achieved by establishing the MGM (1, m) model for the catastrophic date matrix. In order to compare the simulation accuracy and prediction accuracy of the traditional grey catastrophe prediction model with the multivariate grey disaster prediction model, we use an example to simulate and analyze. It can be seen from the result that the multivariate grey disaster prediction model has higher simulation accuracy and prediction accuracy than the traditional grey disaster prediction model. The research indicates that the multivariate grey disaster prediction model complements and improves the grey prediction theory, improving the precision of simulation and prediction. It is worth promoting to other similar multi- variable disaster system.

  • Xinjiang Desertification Disaster Prediction Research Based on Cellular Neural Networks

    At present, the desertification disaster is one of the most serious environmental problems in Xinjiang. In order to reduce the huge losses of desertification disaster to the economic development of Xinjiang, at the same time in order to stability and unity of the people of all ethnic groups in Xinjiang, it is necessary to forecast the future trend of desertification changes. In this paper, taking Ruoqiang basin as an example, we establish desertification prediction model. First, use exponential smoothing model to carry on the numerical simulation for desertification land, the trend of desertification of land, non desertification land area in the area in 2000 -2011 years, Then, we examine forecasts by the MAPE, Desertification of land's MAPE value is 1.96%, have land desertification the land that having a trend of desertification's MAPE value is 7.13%, The predicted result achieves high accuracy prediction effect. Secondly, use exponential smoothing model to predict the land desertification, the desertification trend of land, non desertification land in 2012 -2025 years, we again proved that exponential smoothing model has a huge advantage in the prediction of desertification trends. Finally draws the conclusion: the exponential smoothing model can predict the change trend of desertification land, having the trend of desertification land, non desertification land in the future. The research can provide a powerful reference and guidance for the prevention and control of desertification in the future, but also has theoretical meaning for desertification research in the future.

  • Notice of Retraction<br>Flood inundation and disaster prediction based on DEM

    Flood has two cases, namely “non-source flood” and “source flood”. For each case, flood inundation analysis method based on DEM is discussed. In the paper, Kaiyuan city of the Red River state in Yunnan Province is chosen as experimental area. This region has abundant rainfall, numerous rivers and frequent flood disasters. After calculate the flood area of this region, overlay analysis is used to predict villages threatened by the flood, which using flood area layer and village layer. Finally, Flood inundation map is displayed visually in ArcSence in three dimensions. The result shows that these flood inundation analysis methods are effective and scientifically to simulate flood inundation, which can be a precise reference of loss prediction and guide rescue work.

  • On the use of disaster prediction for failure-tolerance in feedback control systems

    Feedback control algorithms are inherently designed to compensate for external disturbances that the controlled system may suffer. This resilience is also extensible to late or wrong control actions produced by a failed controller computer, providing a degree of fault tolerance without the use of any particular mechanism. However, some controller failures, due to their duration or value, may indeed collapse the system, and thus other recovery measures must be taken. This paper proposes the inclusion of an Oracle that calculates, in a timely manner, the controlled system behavior under a failed controller, and triggers recovery when the control algorithm is predictably no more able to compensate for a particular controller failure. The systems so built follow the Fail-Bounded model. The main contribution of this paper is to show how this model can be implemented in a practical way for the very important class of applications based on feedback control, thus turning that model into a technique that can be used effectively to build production systems. The method was validated experimentally through fault injection on the controller computer of an inverted pendulum, one of the most time-demanding control system benchmarks.

  • Smart flood disaster prediction system using IoT & neural networks

    Floods are the natural disasters that cause catastrophic destruction and devastation of natural life, agriculture, property and infrastructure every year. Flooding is influenced by various hydrological & meteorological factors. A number of researches have been done in flood disaster management and food prediction systems. However, it has now become significant to shift from individual monitoring and prediction frameworks to smart flood prediction systems which include stakeholders and the flood affecting people equally with help of recent technological advancements. Internet of Things (IoT) is a technology that is a combination of embedded system hardware and wireless communication network which further transfers sensed data to computing device for analysis in real-time. Researches in direction of flood prediction have shifted from mathematical models or hydrological models to algorithmic based approaches. Flood data is dynamic data and non-linear in nature. To predict floods, techniques such as artificial neural networks are used to devise prediction algorithms. Here an IoT based flood monitoring and artificial neural network (ANN) based flood prediction is designed with the aim of enhancing the scalability and reliability of flood management system. The main aim of this system is to monitor humidity, temperature, pressure, rainfall, river water level and to find their temporal correlative information for flood prediction analysis. The IoT approach is deployed for data collection from the sensors and communication over Wi-Fi and an ANN approach is used for analysis of data in flood prediction.



Standards related to Disaster Prediction

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