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	Author	Christoph Markmann; Heiko A. Von Der Gracht; Jonas Keller; Rixa Kroehl
	Title	Collaborative foresight as a meansto face future risks – An innovative platform conception			Type	Conference Article
	Year	2012	Publication	ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2012
	Volume		Issue		Pages
	Keywords	Information systems; Brainstorming sessions; Collaboration; Foresight; German federal government; Prediction markets; Requirement analysis; Support systems; Turbulent environments; Commerce
	Abstract	Increasing market volatility and disruptions imply risks for companies and governments and have become therefore focus topics. Adequate tools to identify, assess and manage future developments are key to survive in a turbulent environment. In our paper, we present the systematic development process of an innovative, web-based foresight platform, which is a joint research project funded by the German Federal Government and aims to improve the robustness in decision making by collaborative foresight. Its four interlinked applications have the purpose to enable their users a collaborative generation, discussion, evaluation and development of future-oriented knowledge. Thereby, a special emphasis is on the relevance and the timeliness of the provided information. Within the multi-stage requirement analysis of the tool platform we analyzed existing concepts in order to identify strengths and weaknesses and conducted brainstorming sessions and interviews with professionals of 130 companies and organizations to account for different backgrounds, perspectives and intentions. Â© 2012 ISCRAM.
	Address	Institute for Futures Studies and Knowledge Management (IFK), EBS Business School, Wiesbaden, Germany
	Corporate Author				Thesis
	Publisher	Simon Fraser University	Place of Publication	Vancouver, BC	Editor	L. Rothkrantz, J. Ristvej, Z.Franco
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9780864913326	Medium
	Track	Planning and Foresight	Expedition		Conference	9th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	164
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	Author	Encarnación, T.; Wilks, C.R.
	Title	Role of Expressed Emotions on the Retransmission of Help-Seeking Messages during Disasters			Type	Conference Article
	Year	2023	Publication	Proceedings of the 20th International ISCRAM Conference	Abbreviated Journal	Iscram 2023
	Volume		Issue		Pages	340-352
	Keywords	Social Amplification; Retweet Prediction; Crisis Informatics
	Abstract	Emergency managers rely on formal and informal communication channels to identify needs in post-disaster environments. Message retransmission is a critical factor to ensure that help-seekers are identified by disaster responders. This paper uses a novel annotated dataset of Twitter posts from four major disasters that impacted the United States in 2021, to quantify the effect that expressed emotions and support typology have on retransmission. Poisson regression models are estimated, and the results show that messages seeking instrumental support are more likely to be retransmitted. Expressions of anger, fear, and sadness increase overall retweets. Moreover, expressions of anger, anticipation, or sadness increase the likelihood of retransmission for messages that seek instrumental help.
	Address	College of Business Administration University of Missouri-St
	Corporate Author				Thesis
	Publisher	University of Nebraska at Omaha	Place of Publication	Omaha, USA	Editor	Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi
	Language	English	Summary Language		Original Title
	Series Editor	Hosssein Baharmand	Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition	1
	ISSN		ISBN		Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes	http://dx.doi.org/10.59297/DDXJ4655			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2530
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	Author	Gaoussou Camara; Rim Djedidi; Sylvie Despres; Moussa Lo
	Title	Towards an ontology for an epidemiological monitoring system			Type	Conference Article
	Year	2012	Publication	ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2012
	Volume		Issue		Pages
	Keywords	Disease control; Information systems; Disease spreading; Early prediction; Monitoring system; Ontological modeling; Qualitative approach; Quantitative approach; Risk predictions; Simulation; Monitoring
	Abstract	Epidemiological monitoring systems are used to control the evolution of disease spreading and to suggest action plans to prevent identified risks. In this domain, risk prediction is based on quantitative approaches that are hardly usable when data collection is not possible. In this paper, a qualitative approach based on an epidemiological monitoring ontology is proposed. We describe the design of this ontology and show how it fits into classical monitoring systems and helps overcoming limits related to quantitative approaches. Â© 2012 ISCRAM.
	Address	LANI, UniversitÃ© Gaston Berger, B.P. 234, Saint-Louis, Senegal; LIM and BIO, UniversitÃ© Paris 13, 74 rue Marcel Cachin, 93017 Bobigny, France
	Corporate Author				Thesis
	Publisher	Simon Fraser University	Place of Publication	Vancouver, BC	Editor	L. Rothkrantz, J. Ristvej, Z.Franco
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9780864913326	Medium
	Track	Analytical Modelling and Simulation	Expedition		Conference	9th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	86
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	Author	Guoqin Ma; Chittayong Surakitbanharn
	Title	Predicting Hurricane Damage Using Social Media Posts Coupled with Physical and Socio-Economic Variables			Type	Conference Article
	Year	2019	Publication	Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management	Abbreviated Journal	Iscram 2019
	Volume		Issue		Pages
	Keywords	Social media, disaster management, damage prediction
	Abstract	During a natural disaster or emergency event, individual social media posts or hot spots may not necessarily correlate to the most devastated areas. To better understand the correlation between social media and physical damage, we compare Tweets, data about the physical environment, and socio-economic factors with insurance claim information (as a proxy for physical damage) from 2017 Hurricane Irma in the state of Florida. We use machine learning to identify relevant Tweets, sensitivity analyses to identify socio-economic factors, and statistical regression to determine the predictive capability of insurance claims as a proxy for damage. We find that Tweets alone result in a poorly fitted regression model of insurance claims, but the inclusion of physical features (e.g., power outages, wind level) and socio-economic factors (e.g., population density, education, Internet access) improves the model?s fit. Such models contribute to the knowledge base that may allow social media to predict damage in real-time.
	Address	Stanford University, United States of America
	Corporate Author				Thesis
	Publisher	Iscram	Place of Publication	Valencia, Spain	Editor	Franco, Z.; González, J.J.; Canós, J.H.
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-84-09-10498-7	Medium
	Track	T8- Social Media in Crises and Conflicts	Expedition		Conference	16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
	Notes				Approved	no
	Call Number				Serial	1955
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	Author	Han Che; Shuming Liu
	Title	Monitoring data identification for a water distribution system based on data self-recognition approach			Type	Conference Article
	Year	2013	Publication	ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2013
	Volume		Issue		Pages	166-170
	Keywords	Information systems; Random processes; Statistics; Water distribution systems; ARMA model; Autoregressive moving average model; Contamination events; Data identification; Outlier identification; Prediction confidence; Self-recognition; Water distribution networks; Monitoring
	Abstract	Detecting the occurrence of hydraulic accidents or contamination events in the shortest time has always been a significant but difficult task. The simple and efficient way is to identify the sudden changes or outliers hidden in the vast amounts of monitoring data produced minute by minute, which is unpractical for human. A new method, which employs a data self-recognition approach to achieve that automatically, has been proposed in this paper. The autoregressive moving average (ARMA) model was employed in this research to construct the self-recognition model. 56 months monitoring data from Changping water distribution network in Beijing, which was firstly cut into different time-slice series, was used to establish the ARMA model. This provided a prediction confidence interval in order to identify the outliers in the test data series. The results showed a good performance in outlier identification and the accuracy ranges from 90% to 95%.Thus, the ARMA model showed great potential in dealing with monitoring data and achieving the expected performance of data self-recognition technology.
	Address	Tsinghua University, China
	Corporate Author				Thesis
	Publisher	Karlsruher Institut fur Technologie	Place of Publication	KIT; Baden-Baden	Editor	T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9783923704804	Medium
	Track	Critical Infrastructures	Expedition		Conference	10th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	387
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	Author	Ke Wang; Yongsheng Yang; Genserik Reniers; Jian Li; Quanyi Huang
	Title	An Attribute-based Model to Retrieve Storm Surge Disaster Cases			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	567-580
	Keywords	Storm surge disaster, multiple attributes, retrieval model, affected region prediction
	Abstract	In China, storm surge disasters cause severe damages in coastal regions. One of the most important tasks is to predict affected regions and their relative damage levels to support decision-making. This study develops a two-stage retrieval model to search the most similar past disaster case to complete prediction. Based on spatial attributes of cases, the top-ranking past cases with a similar location to the target case are selected. Among these past cases, the most similar past case is selected by disaster attribute similarities. Three typical storm surge case studies have been used and implemented into this proposed model and the results show that all the most affected regions can be predicted. The proposed model simplifies the prediction process and updates results quickly. This study provides useful information for the government to make real-time response plans.
	Address	Tsinghua University; Tsinghua University; KU Leuven; Tsinghua University; Tsinghua University
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Planning, Foresight and Risk Analysis	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	wangke16@mails.tsinghua.edu.cn			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2356
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	Author	Mehdi Ben Lazreg; Jaziar Radianti; Ole-Christoffer Granmo
	Title	SmartRescue: Architecture for Fire Crisis Assessment and Prediction			Type	Conference Article
	Year	2015	Publication	ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2015
	Volume		Issue		Pages
	Keywords	Bayesian Network; Fire Prediction; Indoor Positioning; Smartphone App
	Abstract	In case of indoor fire hazards, firefighters face difficulties at assessing the fire situation and evacuating trapped victim inside the building, especially when the fire is big, and the building is unknown to them. On the other hand, modern sensor technologies in smartphone are becoming more advanced, widespread, and can be exploited for helping the firefighting operation. This paper proposes using smartphones as a distributed sensing and computing platform, for supporting firefighters to conduct their mission. The developed solution is based on collecting sensor data from smartphones. A Bayesian network then uses this data to generate a picture of the fire and predict its development. The additional indoor positioning feature make this proposed solution a promising tool to make the firefighter intervention more efficient and fast in order to save more lives.
	Address
	Corporate Author				Thesis
	Publisher	University of Agder (UiA)	Place of Publication	Kristiansand, Norway	Editor	L. Palen; M. Buscher; T. Comes; A. Hughes
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9788271177881	Medium
	Track	Decision Support Systems	Expedition		Conference	ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	yes
	Call Number				Serial	1295
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	Author	Shengcheng Yuan; Yi Liu; Gangqiao Wang; Hongshen Sun; H. Zhang
	Title	A dynamic-data-driven driving variability modeling and simulation for emergency evacuation			Type	Conference Article
	Year	2014	Publication	ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2014
	Volume		Issue		Pages	70-74
	Keywords	Computer simulation; Decision making; Information systems; Accurate prediction; Adaptive simulation; Decision making support; Driving variability; Emergency evacuation; Emergency situation; Microscopic traffic simulation; Variability model; Traffic control
	Abstract	This paper presents a dynamic data driven approach of describing driving variability in microscopic traffic simulations for both normal and emergency situations. A four-layer DGIT (Decision, Games, Individual and Transform) framework provides the capability of describing the driving variability among different scenarios, vehicles, time and models. A four-step CCAR (Capture, Calibration, Analysis and Refactor) procedure captures the driving behaviors from mass real-time data to calibrate and analyze the driving variability. Combining the DGIT framework and the CCAR procedure, the system can carry out adaptive simulation in both normal and emergency situations, so that be able to provide more accurate prediction of traffic scenarios and help for decision-making support. A preliminary experiment is performed on a major urban road, and the results verified the feasibility and capability of providing prediction and decision-making support.
	Address	Institute of Public Safety Research, Tsinghua University, Beijing, China; Department of Engineering Physics, Tsinghua University, Beijing, China
	Corporate Author				Thesis
	Publisher	The Pennsylvania State University	Place of Publication	University Park, PA	Editor	S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9780692211946	Medium
	Track	Analytic Modeling and Simulation	Expedition		Conference	11th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1137
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	Author	Simon French; Carmen Niculae
	Title	Believe in the model: Mishandle the emergency			Type	Conference Article
	Year	2004	Publication	Proceedings of ISCRAM 2004 – 1st International Workshop on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2004
	Volume		Issue		Pages	9-14
	Keywords	Artificial intelligence; Civil aviation; Civil defense; Decision making; Decision support systems; Disasters; Forecasting; Information systems; Risk management; Crisis management; Cynefin; Decision support system (dss); Emergency management; Model prediction; Uncertainty; Economic and social effects
	Abstract	During the past quarter century there have been many developments in scientific models and computer codes to help predict the ongoing consequences in the aftermath of many types of emergency: e.g. storms and flooding, chemical and nuclear accident, epidemics such as SARS and terrorist attack. Some of these models relate to the immediate events and can help in managing the emergency; others predict longer term impacts and thus can help shape the strategy for the return to normality. But there are many pitfalls in the way of using these models effectively. Firstly, non-scientists and, sadly, many scientists believe in the models' predictions too much. The inherent uncertainties in the models are underestimated; sometimes almost unacknowledged. This means that initial strategies may need to be revised in ways that unsettle the public, losing their trust in the emergency management process. Secondly, the output from these models form an extremely valuable input to the decision making process; but only one such input. Most emergencies are events that have huge social and economic impacts alongside the health and environmental consequences. While we can model the latter passably well, we are not so good at modelling economic impacts and very poor at modelling social impacts. Too often our political masters promise the best 'science-based' decision making and too late realise that the social and economic impacts need addressing. In this paper, we explore how model predictions should be drawn into emergency management processes in more balanced ways than often has occurred in the past. Â© Proceedings ISCRAM 2004.
	Address	Manchester Business School, University of Manchester, Booth Street West, Manchester M15 6PB, United Kingdom
	Corporate Author				Thesis
	Publisher	Royal Flemish Academy of Belgium	Place of Publication	Brussels	Editor	B. Van de Walle, B. Carle
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9076971080	Medium
	Track	Conference Keynote	Expedition		Conference	1st International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	111
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	Author	Songhui Yue; Jyothsna Kondari; Aibek Musaev; Songqing Yue; Randy Smith
	Title	Using Twitter Data to Determine Hurricane Category: An Experiment			Type	Conference Article
	Year	2018	Publication	ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2018
	Volume		Issue		Pages	718-726
	Keywords	Social Media Data, Hurricane Category, Twitter, Prediction
	Abstract	Social media posts contain an abundant amount of information about public opinion on major events, especially natural disasters such as hurricanes. Posts related to an event, are usually published by the users who live near the place of the event at the time of the event. Special correlation between the social media data and the events can be obtained using data mining approaches. This paper presents research work to find the mappings between social media data and the severity level of a disaster. Specifically, we have investigated the Twitter data posted during hurricanes Harvey and Irma, and attempted to find the correlation between the Twitter data of a specific area and the hurricane level in that area. Our experimental results indicate a positive correlation between them. We also present a method to predict the hurricane category for a specific area using relevant Twitter data.
	Address
	Corporate Author				Thesis
	Publisher	Rochester Institute of Technology	Place of Publication	Rochester, NY (USA)	Editor	Kees Boersma; Brian Tomaszeski
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-0-692-12760-5	Medium
	Track	Social Media Studies	Expedition		Conference	ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	2145
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	Author	Sung-Yueh Perng; Monika Büscher
	Title	Uncertainty and Transparency: Augmenting Modelling and Prediction for Crisis Respons			Type	Conference Article
	Year	2015	Publication	ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2015
	Volume		Issue		Pages
	Keywords	Collaboration; modelling prediction; reasoning; transparency; uncertainty
	Abstract	Emergencies are characterised by uncertainty. This motivates the design of information systems that model and predict complex natural, material or human processes to support understanding and reduce uncertainty through prediction. The correspondence between system models and reality, however, is also governed by uncertainties, and designers have developed methods to render ?the world? transparent in ways that can inform, fine-tune and validate models. Additionally, people experience uncertainties in their use of simulation and prediction systems. This is a major obstacle to effective utilisation. We discuss ethically and socially motivated demands for transparency.
	Address
	Corporate Author				Thesis
	Publisher	University of Agder (UiA)	Place of Publication	Kristiansand, Norway	Editor	L. Palen; M. Buscher; T. Comes; A. Hughes
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9788271177881	Medium
	Track	Ethical, Legal and Social Issues	Expedition		Conference	ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1200
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	Author	Tiberiu Sosea; Iustin Sirbu; Cornelia Caragea; Doina Caragea; Traian Rebedea
	Title	Using the Image-Text Relationship to Improve Multimodal Disaster Tweet Classification			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	691-704
	Keywords	Multi-modal disaster tweet classification, Image-text coherence relationship prediction, ViLBERT
	Abstract	In this paper, we show that the text-image relationship of disaster tweets can be used to improve the classification of tweets from emergency situations. To this end, we introduce DisRel, a dataset which contains 4,600 multimodal tweets, collected during the disasters that hit the USA in 2017, and manually annotated with coherence image-text relationships, such as Similar and Complementary. We explore multiple models to detect these relationships and perform a comprehensive analysis into the robustness of these methods. Based on these models, we build a simple feature augmentation approach that can leverage the text-image relationship. We test our methods on 2 tasks in CrisisMMD: Humanitarian Categories and Damage Assessment, and observe an increase in the performance of the relationship-aware methods.
	Address	University of Illinois at Chicago; University Politehnica of Bucharest; University of Illinois at Chicago; Kansas State University; University Politehnica of Bucharest
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Social Media for Disaster Response and Resilience	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	tsosea2@uic.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2365
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	Author	Venkata Kishore Neppalli; Murilo Cerqueira Medeiros; Cornelia Caragea; Doina Caragea; Andrea Tapia; Shane Halse
	Title	Retweetability Analysis and Prediction during Hurricane Sandy			Type	Conference Article
	Year	2016	Publication	ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2016
	Volume		Issue		Pages
	Keywords	Twitter; Retweetability Analysis; Retweetability Prediction; Hurricane Sandy; Disaster Events
	Abstract	Twitter is a very important source for obtaining information, especially during events such as natural disasters. Users can spread information in Twitter either by crafting new posts, which are called ?tweets,? or by using retweet mechanism to re-post the previously created tweets. During natural disasters, identifying how likely a tweet is to be highly retweeted is very important since it can help promote the spread of good information in a network such as Twitter, as well as it can help stop the spread of misinformation, when corroborated with approaches that identify trustworthy information or misinformation, respectively. In this paper, we present an analysis on retweeted tweets to determine several aspects affecting retweetability. We then extract features from tweets? content and user account information and perform experiments to develop models that automatically predict the retweetability of a tweet in the context of the Hurricane Sandy.
	Address
	Corporate Author				Thesis
	Publisher	Federal University of Rio de Janeiro	Place of Publication	Rio de Janeiro, Brasil	Editor	A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3388	ISBN	978-84-608-7984-9	Medium
	Track	Social Media Studies	Expedition		Conference	13th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1389
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	Author	Xiaoyong Ni; Hong Huang; Wenxuan Dong; Chao Chen; Boni Su; Anying Chen
	Title	Scenario Prediction and Crisis Management for Rain-induced Waterlogging Based on High-precision Simulation			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	159-173
	Keywords	Rain-induced waterlogging, Scenario prediction, High-precision simulation, Crisis management
	Abstract	Many cities, especially those in developing countries, are not well prepared for the devastating disaster of exceptional rain-induced waterlogging caused by extreme rainfall. This paper proposes a waterlogging scenario prediction and crisis management method for such kind of extreme rainfall conditions based on high-precision waterlogging simulation. A typical urban region in Beijing, China is selected as the study area in this paper. High-precision and full-scale data in the study area requested for the waterlogging simulation are introduced. The simulation results show that the study area is still vulnerable to extreme rainfall and the subsequent waterlogging. The waterlogging situation is much more severe with the increase of the return period of rainfall. This study offers a good reference for the relevant government departments to make effective policy and take pointed response to the waterlogging problem.
	Address	Tsinghua University; Tsinghua University; Tsinghua University; Beijing Water Authority; Electric Power Planning & Engineering Institute; Tsinghua University
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Analytical Modeling and Simulation	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	nxy15@mails.tsinghua.edu.cn			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2322
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