Although Twitter is used for emergency management activities,the relevance of tweets during a hazard event is still open to debate.In this study,six different computational(i.e.Natural Language Processing)and spatiote...Although Twitter is used for emergency management activities,the relevance of tweets during a hazard event is still open to debate.In this study,six different computational(i.e.Natural Language Processing)and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event.Primarily,tweets containing information about the flooding events and its impacts were analysed.Examination of the relationships between tweet volume and its content with precipitation amount,damage extent,and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages.However,only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event.By providing insight into the quality of social media data and its usefulness to emergency management activities,this study contributes to the literature on quality of big data.Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.展开更多
Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainabil-ity.To this end,recent technological advancement has allowed the production of la...Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainabil-ity.To this end,recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors.We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability,transport,and use to energy generation,fuel supply,and customer demand,and in the interde-pendencies among these systems that can leave these systems vul-nerable to cascading impacts from single disruptions.In this paper,we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves.We then survey machine learning techniques that have found application to date in energy-water nexus problems.We con-clude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.展开更多
Social media,including Twitter,has become an important source for disaster response.Yet most studies focus on a very limited amount of geotagged data(approximately 1%of all tweets)while discarding a rich body of data ...Social media,including Twitter,has become an important source for disaster response.Yet most studies focus on a very limited amount of geotagged data(approximately 1%of all tweets)while discarding a rich body of data that contains location expressions in text.Location information is crucial to understanding the impact of disasters,including where damage has occurred and where the people who need help are situated.In this paper,we propose a novel two-stage machine learningand deep learning-based framework for power outage detection from Twitter.First,we apply a probabilistic classification model using bag-ofngrams features to find true power outage tweets.Second,we implement a new deep learning method-bidirectional long short-term memory networks-to extract outage locations from text.Results show a promising classification accuracy(86%)in identifying true power outage tweets,and approximately 20 times more usable tweets can be located compared with simply relying on geotagged tweets.The method of identifying location names used in this paper does not require language-or domain-specific external resources such as gazetteers or handcrafted features,so it can be extended to other situational awareness analyzes and new applications.展开更多
The energy-water nexus,or the dependence of energy on water and water on energy,continues to receive attention as impacts on both energy and water supply and demand from growing popula-tions and climate-related stress...The energy-water nexus,or the dependence of energy on water and water on energy,continues to receive attention as impacts on both energy and water supply and demand from growing popula-tions and climate-related stresses are evaluated for future infra-structure planning.Changes in water and energy demand are related to changes in regional temperature,and precipitation extremes can affect water resources available for energy genera-tion for those regional populations.Additionally,the vulnerabilities to the energy and water nexus are beyond the physical infrastruc-tures themselves and extend into supporting and interdependent infrastructures.Evaluation of these vulnerabilities relies on the integration of the disparate and distributed data associated with each of the infrastructures,environments and populations served,and robust analytical methodologies of the data.A capability for the deployment of these methods on relevant data from multiple components on a single platform can provide actionable informa-tion for interested communities,not only for individual energy and water systems,but also for the system of systems that they com-prise.Here,we survey the highest priority data needs and analy-tical methods for inclusion on such a platform.展开更多
基金funded partially by the National Science Foundation[grant no CMMI-1335187]the Department of Homeland Security Contract[grant no HSHQDC-12-C-00057]the 2014,2015,and 2016 Arthell Kelley Scholarships from the Department of Geography and Geology at The University of Southern Mississippi.
文摘Although Twitter is used for emergency management activities,the relevance of tweets during a hazard event is still open to debate.In this study,six different computational(i.e.Natural Language Processing)and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event.Primarily,tweets containing information about the flooding events and its impacts were analysed.Examination of the relationships between tweet volume and its content with precipitation amount,damage extent,and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages.However,only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event.By providing insight into the quality of social media data and its usefulness to emergency management activities,this study contributes to the literature on quality of big data.Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.
基金This manuscript has been authored by employees of UT- Battelle, under contract DE AC05-000R22725 with the US Department of Energy. The authors would also like to acknowledge thefinancial and intellectual support for this research by the Integrated Assessment Research Programof the US Department of Energy's Office of Science, Biological and Environmental Research. Thiswork is supported in part by NSF ACI-1541215.
文摘Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainabil-ity.To this end,recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors.We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability,transport,and use to energy generation,fuel supply,and customer demand,and in the interde-pendencies among these systems that can leave these systems vul-nerable to cascading impacts from single disruptions.In this paper,we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves.We then survey machine learning techniques that have found application to date in energy-water nexus problems.We con-clude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.
基金the financial support received from Oak Ridge National Laboratory(ORNL)’s Liane Russell Distinguished Early Career Fellowship and grant no.TG0100000.
文摘Social media,including Twitter,has become an important source for disaster response.Yet most studies focus on a very limited amount of geotagged data(approximately 1%of all tweets)while discarding a rich body of data that contains location expressions in text.Location information is crucial to understanding the impact of disasters,including where damage has occurred and where the people who need help are situated.In this paper,we propose a novel two-stage machine learningand deep learning-based framework for power outage detection from Twitter.First,we apply a probabilistic classification model using bag-ofngrams features to find true power outage tweets.Second,we implement a new deep learning method-bidirectional long short-term memory networks-to extract outage locations from text.Results show a promising classification accuracy(86%)in identifying true power outage tweets,and approximately 20 times more usable tweets can be located compared with simply relying on geotagged tweets.The method of identifying location names used in this paper does not require language-or domain-specific external resources such as gazetteers or handcrafted features,so it can be extended to other situational awareness analyzes and new applications.
基金This work was supported by the Integrated Assessment Research Program of the US Department of Energy’s Office of ScienceBiological and Environmental Research+1 种基金Department of Energy Office of PolicyNSF ACI-1541215.
文摘The energy-water nexus,or the dependence of energy on water and water on energy,continues to receive attention as impacts on both energy and water supply and demand from growing popula-tions and climate-related stresses are evaluated for future infra-structure planning.Changes in water and energy demand are related to changes in regional temperature,and precipitation extremes can affect water resources available for energy genera-tion for those regional populations.Additionally,the vulnerabilities to the energy and water nexus are beyond the physical infrastruc-tures themselves and extend into supporting and interdependent infrastructures.Evaluation of these vulnerabilities relies on the integration of the disparate and distributed data associated with each of the infrastructures,environments and populations served,and robust analytical methodologies of the data.A capability for the deployment of these methods on relevant data from multiple components on a single platform can provide actionable informa-tion for interested communities,not only for individual energy and water systems,but also for the system of systems that they com-prise.Here,we survey the highest priority data needs and analy-tical methods for inclusion on such a platform.
