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Aikaterini Christodoulou, John Lioumbas, Kostantinos Zambetoglou, & Nikoletta Xanthopoulou. (2021). Combined innovative technologies for ensuring water safety in utilities: The city of Thessaloniki case study. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 929–934). Blacksburg, VA (USA): Virginia Tech.
Abstract: Innovative technologies such as monitoring the quality of surface water aquifers with satellite images, applying UAV (Unmanned Aerial Vehicle) and drone technology for a variety of operations, water quality measurements with improved techniques along with IoT (Internet of Things) and ICT (Information and Communication Technologies), can provide sufficient data for enhancing water safety in urban water utilities. Specifically, these data could be an effective tool for improving risk assessment process and management of water supply systems. Nevertheless, till now, there is a relative lack of published works that validate the efficiency of combing these technologies on water safety processes by incorporating most of them with a systematic way and during real working conditions in water utilities. This work aims to present the preliminary design concept of a platform that embraces innovating water safety technologies planned to be applied to Thessaloniki's Water Supply and Sewerage Co. S.A Standard Operating Procedures (SOP).
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Thomas Bernard, Mathias Braun, Olivier Piller, Denis Gilbert, Jochen Deuerlein, Andreas Korth, et al. (2013). SMaRT-OnlineWDN: Online security management and reliability toolkit for water distribution networks. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 171–176). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Water distribution Networks (WDNs) are critical infrastructures that are exposed to deliberate or accidental contamination. Until now, no monitoring system is capable of protecting a WDN in real time. In the immediate future water service utilities that are installing water quantity and quality sensors in their networks will be producing a continuous and huge data stream for treating. The main objective of the project SMaRT-OnlineWDN is the development of an online security management toolkit for water distribution networks that is based on sensor measurements of water quality as well as water quantity and online simulation. Its field of application ranges from detection of deliberate contamination, including source identification and decision support for effective countermeasures, to improved operation and control of a WDN under normal and abnormal conditions.
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Hüseyin Can Ünen, Muhammed Sahin, & Amr S. Elnashai. (2011). Assessment of interdependent lifeline networks performance in earthquake disaster management. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Several studies and observations regarding past earthquakes such as 1989 Loma Prieta, 1994 Northridge, or 1999 Marmara earthquakes have shown the importance of lifeline systems functionality on response and recovery efforts. The general direction of studies on simulating lifelines seismic performance is towards achieving more accurate models to represent the system behavior. The methodology presented in this paper is a product of research conducted in the Mid-America Earthquake Center. Electric power, potable water, and natural gas networks are modeled as interacting systems where the state of one network is influenced by the state of another network. Interdependent network analysis methodology provides information on operational aspects of lifeline networks in post-seismic conditions in addition to structural damage assessment. These results are achieved by different components of the tool which are classified as structural and topological. The topological component analyzes the post seismic operability of the lifeline networks based on the damage assessment outcome of the structural model. Following an overview of the models, potential utilizations in different phases of disaster management are briefly discussed.
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Cedric Papion. (2018). Water supply network resilience in the Wellington Region. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 263–271). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: Wellington sits across an active seismic fault line and depends on remote sources for its water supply. With widespread damage expected after a large earthquake, it may be months before a minimal water supply is restored to residents, and even longer before it reaches the tap. This paper presents a recent study undertaken to identify network vulnerabilities and take water supply resilience to the next level. The study presented a possible timeline for repairs to the bulk network and restoration of supply to each suburb's reservoir. This highlighted the most critical areas where an alternative supply or storage was needed. The study also considered how to get the water to the customers after the reticulation network had been damaged. The strategy considered by Wellington Water was to develop a seismically-resilient skeleton network connecting reservoirs and key distribution points. A notable innovation was the use of algorithms to determine optimal locations for public tap stands and identify the most cost-effective critical pipe network where strengthening upgrades needed to be focused. The aspects of the project concerning its significance for the region, the overall resilience strategy and the pipeline resilience engineering were presented at the Institute of Public Works Engineering Australasia (IPWEA) and Water NZ conferences in 2017. While this paper touches on these subjects, its main focus is on the use of geospatial information for earthquake preparedness and resilience planning.
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Han Che, & Shuming Liu. (2013). Monitoring data identification for a water distribution system based on data self-recognition approach. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 166–170). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
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.
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Cheng Wang, Benjamin Bowes, Arash Tavakoli, Stephen Adams, Jonathan Goodall, & Peter Beling. (2020). Smart Stormwater Control Systems: A Reinforcement Learning Approach. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 2–13). Blacksburg, VA (USA): Virginia Tech.
Abstract: Flooding poses a significant and growing risk for many urban areas. Stormwater systems are typically used to control flooding, but are traditionally passive (i.e. have no controllable components). However, if stormwater systems are retrofitted with valves and pumps, policies for controlling them in real-time could be implemented to enhance system performance over a wider range of conditions than originally designed for. In this paper, we propose an autonomous, reinforcement learning (RL) based, stormwater control system that aims to minimize flooding during storms. With this approach, an optimal control policy can be learned by letting an RL agent interact with the system in response to received reward signals. In comparison with a set of static control rules, RL shows superior performance on a wide range of artificial storm events. This demonstrates RL's ability to learn control actions based on observation and interaction, a key benefit for dynamic and ever-changing urban areas.
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Simone De Kleermaeker, & Jan Verkade. (2013). A decision support system for effective use of probability forecasts. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 290–295). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Often, water management decisions are based on hydrological forecasts, which are affected by inherent uncertainties. It is increasingly common for forecasters to make explicit estimates of these uncertainties. Associated benefits include the decision makers' increased awareness of forecasting uncertainties and the potential for risk-based decision-making. Also, a more strict separation of responsibilities between forecasters and decision maker can be made. A recent study identified some issues related to the effective use of probability forecasts. These add a dimension to an already multi-dimensional problem, making it increasingly difficult for decision makers to extract relevant information from a forecast. Secondly, while probability forecasts provide a necessary ingredient for risk-based decision making, other ingredients may not be fully known, including estimates of flood damage and costs and effect of damage reducing measures. Here, we present suggestions for resolving these issues and the integration of those solutions in a prototype decision support system (DSS). A pathway for further development is outlined.
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Lívia C. Degrossi, Guilherme G. Do Amaral, Eduardo S. M. De Vasconcelos, João Porto De Albuquerque, & Jo Ueyama. (2013). Using wireless sensor networks in the sensor web for flood monitoring in Brazil. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 458–462). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Flood is a critical problem that will increase as a result of climate changes. The problem of flooding is particularly challenging over the rainy season in tropical countries like Brazil. In this context, wireless sensor networks that are capable of sensing and reacting to water levels hold the potential of significantly reducing the damage, health-risks and financial impact of events. In this paper, we aim to outline our experiences with developing wireless sensor network for flood monitoring in Brazil. Our approach is based on Open Geospatial Consortium's (OGC) Sensor Web Enablement (SWE) standards, so as to enable the collected data to be shared in an interoperable and flexible manner. We describe the application of our approach in a real case study in the city of São Carlos/Brazil, emphasizing the challenges involved, the results achieved, and some lessons learned along the way.
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Edjossan-Sossou, A., Selouane, K., Sayah, M. A., Ouabou, M., Vignote, C., Capitaine, M., et al. (2023). An innovative scenario-based modeling tool for the management of resilient water resources. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 808–821). Omaha, USA: University of Nebraska at Omaha.
Abstract: As freshwater availability for domestic and agro-industrial uses is highly sensitive to climate change, there is an urgent need for the management of this critical resource to be resilient, i.e., to cope with and rapidly recover from climate risks. To achieve this resilient goal, decision-makers need to have a comprehensive understanding of (i) the current and future local water resources, (ii) the ways these resources are and will be impacted by climate change, and (iii) the effects their management decisions can have. In this paper, we present an innovative scenario based modeling tool that help decision-makers make the most appropriate decision towards managing water resources: the Resilience Performance Assessment (RPA). This GIS-based decision support tool illustrates the current and future effects of climate change on local water resources and simulates the outcomes of different water resources management strategies. The RPA helps guide decision-makers towards the implementation of context specific adaptation strategies.
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Daniel Hahn. (2007). Non-restrictive linking in wireless sensor networks for industrial risk management. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 605–609). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The OSIRIS project addresses the disaster management workflow in the phases of risk monitoring and crisis management. Risk monitoring allows the continuous observation of endangered areas combined with sensor deployment strategies. The crisis management focuses on particular events and the support by sensor networks. Four complementary live demonstrations will validate the OSIRIS approach. These demonstrations include water contamination, air pollution, south European forest fire, and industrial risk monitoring. This paper focuses on the latter scenario: the industrial risk monitoring. This scenario offers the special opportunity to demonstrate the relevance of OSIRIS by covering all the aspects of monitoring, preparation and response phases of both environmental risk and crisis management. The approach focuses on non-restrictive linking in a wireless sensor network in order to facilitate the addition and removal of nodes providing open interaction primitives allowing the comfortable integration, exclusion, and modification. A management layer with an event-triggered and service-based middleware is proposed. A live lab with real fire is illustrated.
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Benjamin Herfort, João Porto De Albuquerque, Svend-Jonas Schelhorn, & Alexander Zipf. (2014). Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 747–751). University Park, PA: The Pennsylvania State University.
Abstract: In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring.
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Alicia Cabañas Ibañez, Dirk Schwanenberg, Luis Garrote De Marcos, Miguel Francés Mahamud, & Javier Arbaizar González. (2011). An example of Flood Forecasting and Decision-Support System for water management in Spain. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The paper provides an overview of past, present and future development in the program to implement a Flood Forecasting and Decision-Support System (DSS) for the SAIH network in some Spanish basins. These tools represent a significant advance by embedding the decision-making components for management of hydraulic infrastructure into the flood forecasting and flood early warning procedures. The DSS has been implemented based on an open-shell platform for integrating various data sources and different simulation models. So far, it covers the Segura, Jucar, Tajo, Duero and Miño-Sil basins, which represent 42% of Spanish territory. Special attention is paid to the decision-support for the operation of the 66 major reservoirs as a fundamental part of flood management.
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Syed Imran, Franclin Foping, Ioannis M. Dokas, & John Feehan. (2010). Towards domain specific modeling approach in early warning system. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: It is of practical significance and great value to design and develop a novel Early Warning System (EWS), which will be used by the personnel of institutions involved in the drinking water delivery governance model of Ireland. In order to help the users of our EWS in representing and codifying their knowledge on the complex coincidences that may drive Water Treatment Plants (WTP) to failures or to hazardous states we propose in this paper a novel approach of using Domain Specific Modeling (DSM) in the domain of EWS for Water Treatment Plants. The novelty of our DSM approach also lies in providing a standalone open source software application rendering profiling of the water utilities, early warning signals, monitoring mechanisms of signals along with capability of assessing the “tendency” of a WTP towards failure, given a set of observed early warning signals.
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Vitaveska Lanfranchi, Stuart N. Wrigley, Neil Ireson, Uta Wehn, & Fabio Ciravegna. (2014). Citizens' observatories for situation awareness in flooding. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 145–154). University Park, PA: The Pennsylvania State University.
Abstract: Citizens' observatories are emerging as a means to establish interaction and co-participation between citizens and authorities during both emergencies and the day-to-day management of fundamental resources. In this paper we present a case study in which a model of citizens' observatories is being been translated into practice in the WeSenseIt project. The WeSenseIt citizens' observatory provides a unique way of engaging the public in the decision-making processes associated with water and flood management through a set of new digital technologies. The WeSenseIt citizens' observatory model is being implemented in three case studies based in the UK, the Netherlands and Italy. We describe the findings and our experiences following preliminary evaluations of the technologies and the model of co-participation and describe our future research plans.
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Mirko Zaffaroni, & Claudio Rossi. (2020). Water Segmentation with Deep Learning Models for Flood Detection and Monitoring. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 66–74). Blacksburg, VA (USA): Virginia Tech.
Abstract: Flooding is a natural hazard that causes a lot of deaths every year and the number of flood events is increasing worldwide because of climate change effects. Detecting and monitoring floods is of paramount importance in order to reduce their impacts both in terms of affected people and economic losses. Automated image analysis techniques capable to extract the amount of water from a picture can be used to create novel services aimed to detect floods from fixed surveillance cameras, drones, crowdsourced in-field observations, as well as to extract meaningful data from social media streams. In this work we compare the accuracy and the prediction performances of recent Deep Learning algorithms for the pixel-wise water segmentation task. Moreover, we release a new dataset that enhances well-know benchmark datasets used for multi-class segmentation with specific flood-related images taken from drones, in-field observations and social media.
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Pengfei Zhou, Tao Chen, Guofeng Su, Bingxu Hou, & Lida Huang. (2020). Research on the Forecasting and Risk Analysis Method of Snowmelt Flood. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 545–557). Blacksburg, VA (USA): Virginia Tech.
Abstract: Risk analysis of snowmelt flood is an urgent demand in cold highland areas. This paper focuses on the method for the rapid and reliable forecast of daily snowmelt, snow water runoff, and snowmelt flood risk. A neural network algorithm is used to calculate snow density distribution, snow depth and snow-water equivalent with the brightness temperature data. Then, daily snowmelt is predicted using the degree-day factor method with the temperature distribution. On this basis, we use the steepest descent method and Manning formula with hydrographic information to simulate snow water runoff. We also propose a method to predict the snowmelt flood risk with the geographic feature and historical flood data. The evaluated risk is compared with monitored data in the Xinjiang Autonomous Region of China, which shows good consistency. At last, we develop a risk analysis system to generate the snowmelt flood risk map and provide risk analysis service.
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Maria A. Santos, António Gonçalves, Sandra Silva, Nuno Charneca, & Miguel Gamboa. (2004). Dam break emergency response Information System. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2004 – 1st International Workshop on Information Systems for Crisis Response and Management (pp. 27–32). Brussels: Royal Flemish Academy of Belgium.
Abstract: Although considered of low risk, incidents with dams may cause significant damage both directly and indirectly. Direct losses are usually easier to assess (assuming human lives are quantifiable), but indirect losses are difficult to measure and may take some time before the original situation is restored. Disaster prevention and vulnerability reduction have been topics of major concern in many local, national or international organisations for some years. These can be accomplished through emergency management which begins with hazard identification and planning for disaster mitigation but encompasses other activities as risk analysis, risk response and recovery. Therefore, an emergency management system with capacity to: i) forecast critical situations; ii) warn the population as well as the authorities; and iii) support the civil protection system to deal with an emergency, is a most helpful tool to minimize the impact of an accident. The Information System described herein fulfils mainly the third objective, i.e. it is intended to help the Civil Protection System in Portugal, to respond to an emergency caused by the failure of a dam. It is an Internet-based application, which integrates all relevant data for the implementation of a dam emergency plan. These data include the main characteristics of the dam and its reservoir, the character-isation of the dam downstream valley as well as the response procedures to be followed in an emergency. © Proceedings ISCRAM 2004.
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Krispijn Scholte, & Leon J.M. Rothkrantz. (2014). Personal warning system for vessels under bad weather conditions. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 359–368). University Park, PA: The Pennsylvania State University.
Abstract: Many services provide weather forecasts, including severe weather alerts for the marine. It proves that many ships neglect the warnings because they expect to be able to handle the bad weather conditions. In order to identify possible unsafe situations the Coast Guard needs to observe marine vessel traffic 24 hours, 7 days a week. In this paper we propose a system that is able to support the Coast Guard. Ships can be localized and tracked individually using the Automatic Identification System (AIS). We present a system which is able to send a personal alert to ships expected to be in danger now or the near future. Ships will be monitored in the dangerous hours and routed to safe areas in the shortest time. The system is based on AIS data, probabilistic reasoning and expertise from the Coast Guard. A first prototype will be presented for open waters around the Netherlands.
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Toshihiro Osaragi, & Noriaki Hirokawa. (2019). Simulation Analysis of Fire Hydrant Usability Levels after Large Earthquake. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Since large earthquakes can disrupt water supply networks, it is essential to gain an understanding of the expected
usability of fire hydrants in post-quake firefighting activities. In this study, data about water supply networks was
collected and a water outage simulation model was constructed in order to predict the likelihood that individual
fire hydrants would become unusable in the wake of a large earthquake. The water outage simulation model was
integrated with a previously developed urban zone damage simulation and a fire department activity simulation
in order to carry out a simulation-based analysis of the 23 wards of Tokyo, after which a quantitative analysis of
the relation-ship between use of fire hydrants and the number of buildings lost to fire was performed. This analysis
revealed the benefits of hardening water lines against earthquakes, fire hydrant usage variations depending on
locality, and the benefits of using water pressure sensors to identify usable fire hydrants.
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Toshihiro Osaragi, Koji Ogino, Noriaki Hirokawa, & Takuya Oki. (2022). Severity of Crowding at Evacuation Shelters after a Major Earthquake. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 22–43). Tarbes, France.
Abstract: A number of residents are presumed to evacuate to shelters after a large earthquake. However, the congestion of evacuation shelters has not been enough discussed. In this paper, we propose an evacuation behavior model, which includes sub-models on building damage, water-supply failure, power failure, fire damage, and elevator stall. Using the model estimated using the survey data of the past earthquakes, we discuss the congestion of evacuation shelters under the assumption of Tokyo Bay northern earthquake. Finally, we discuss improvement of water pipes for earthquake resistance to reduce the congestion degree of evacuation shelters, which varies according to regional vulnerability.
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Daniel Twigt, João Lima Rego, Deborah Tyrrell, & Tineke Troost. (2011). Water quality forecasting systems: Advanced warning of harmful events and dissemination of public alerts. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Operational systems developed to monitor and forecast water quality can play a key role to counter and reduce the impact of harmful water quality events. Through these systems, many of the steps required to provide relevant information to the water quality manager can be automated, reducing the lead time required for a warning to be issued, as well as the potential for human error. The systems can also facilitate the routine dissemination of water quality forecasts to relevant parties in order to trigger early warnings or crisis response. This paper outlines some general characteristics of such water quality forecasting systems, focusing on the various elements from which such systems are composed. In addition, examples of existing systems to forecast bathing water quality and harmful algae blooms are provided as illustration. Such systems are either in a development stage (bathing water quality) or already used in operations (harmful algae blooms).
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Victor A. Bañuls, Andrzej M. Skulimowski, & José Antonio Román Begines. (2021). Disaster Resilience Modeling of Municipal Water Supply Infrastructures in the Context of Atmospheric Threats. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 198–207). Blacksburg, VA (USA): Virginia Tech.
Abstract: The resilience of water supply infrastructure (WSI) is of utmost importance as threats to predominantly, although not exclusively, urban WSI may accompany virtually all kinds of natural disasters. In this paper, we present some of the challenges posed by climate change in modeling emergencies in WSIs. Climate change is a global phenomenon that significantly impacts global lifestyle. It is expected that increase in global temperatures causes sea levels to rise, increases the number of extreme weather events such as floods, droughts, and storms while highly impacting WSI. In this respect, the challenge is to be prepared for the unexpended by modeling various complex scenarios. Only with a multidisciplinary approach at the global, regional, national, and local levels, can success be achieved. We discuss some of the specific challenges posed by climate change in modeling emergencies in WSIs with a case study modeled using EMERTIC. EMERTIC is a software based on AI and scenarios, that is aimed at supporting decision making at different stages of the Emergency Management cycle.
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Karolina A. Wojciechowska, & Berend Vreugdenhil. (2012). Integration of uncertainty into emergency procedures of water boards. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: In the Netherlands, Royal Dutch Meteorological Institute warns water boards for extreme rainfall if per-specified thresholds are (expected to be) exceeded. When a water board receives a warning, certain response measures can be taken. In general, the thresholds are based on experience and intuition. Clear procedures, which describe decision-making under uncertainty in available information (e.g., forecasted rainfall), do not exist. In this document, first results of the project “Extreme weather for water boards” are briefly described. The aim of this project is to study integration of the uncertainty into emergency procedures of the water boards. The current emergency procedures of two water boards are analyzed. Recommended adjustments to the procedures allow including the uncertainty by estimation of a probability of overload and cost-benefit analysis of response measures (benefit as avoided damage). A simple scheme that supports estimation of the probability is introduced. © 2012 ISCRAM.
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Xiaoyong Ni, Hong Huang, Shiwei Zhou, Boni Su, Jianchun Zheng, Wei Zhu, et al. (2018). Simulation of The Urban Waterlogging and Emergency Response Strategy at Subway Station's Entry-exit Platform in Heavy Rainstorm. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 99–120). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Underground space like subway stations is prone to be flooded which can lead to severe and unpredictable damage and even threaten human lives. In this paper, four groups of contrastive simulation of urban waterlogging at two subway stations' entry-exit platforms in heavy rainstorm are conducted, and emergency response strategies are suggested. A waterlogging simulation method named UPFLOOD based on shallow water equations is proposed considering complex topography. It has been found that the waterlogging at subway station's entry-exit platforms is easily influenced by several factors and the site selection of the subway stations is very important. A disaster process construction method based on PN model is proposed and it has been found that the response strategies including plugging, drainage and evacuation are important for disaster mitigation. This study helps decision makers to response quickly to meet the emergency of the waterlogging disaster at subway stations caused by heavy rainstorm.
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Xiaoyong Ni, Hong Huang, Wenxuan Dong, Chao Chen, Boni Su, & Anying Chen. (2021). Scenario Prediction and Crisis Management for Rain-induced Waterlogging Based on High-precision Simulation. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 159–173). Blacksburg, VA (USA): Virginia Tech.
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.
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