Using sensor web technologies to help predict and monitor floods in urban areas

Includes abstract.Includes bibliographical references.Since flooding is worldwide one of the most common natural disasters, a number of flood prediction and monitoring approaches have been used. A lot of research has been conducted on the prediction and monitoring of floods by using hydrological models. The problem is that current hydrological models do not offer Disaster Management officials or township residents with timely data and information. In South Africa, possible flood warnings are usually communicated by Disaster Management officials using traditional approaches such as loudspeakers, radio and Television (TV). Making calls to warn residents about the possible occurrence of floods by using such means are, however, neither sufficient nor effective. As the result of improved communication, sensor, software and computing capabilities, the use of sensor networks and sensor web for predicting and monitoring environment have been considered in recent years. In order for sensor data such as sensor measurements, sensor descriptions and alerts to be integrated, the Open Geospatial Consortium (OGC) introduced the Sensor Web enablement (SWE) standards and suggested different specifications with respect to the geospatial sensor web. The first implementation of the sensor web framework is available. In this research, the results of using the sensor web technologies for predicting and monitoring floods in the urban areas are presented. The aim of this research project is to illustrate how the sensor web technology can help in the prediction and monitoring of floods in the urban areas, particularly in the Alexandra Township (Greater Johannesburg) which has experienced floods each and every year. The focus of this research is on the incorporation of the sensor data into the sensor web technology. The data used as input to sensor web and the hydrological model was historical rainfall data from the South African Weather Service (SAWS). Shuttle Radar Topography Mission (SRTM) free data from the internet was also used in this research

Real-time early warning system design for pluvial flash floods - A review

[EN] Pluvial flash floods in urban areas are becoming increasingly frequent due to climate change and human actions, negatively impacting the life, work, production and infrastructure of a population. Pluvial flooding occurs when intense rainfall overflows the limits of urban drainage and water accumulation causes hazardous flash floods. Although flash floods are hard to predict given their rapid formation, Early Warning Systems (EWS) are used to minimize casualties. We performed a systematic review to define the basic structure of an EWS for rain flash floods. The structure of the review is as follows: first, Section 2 describes the most important factors that affect the intensity of pluvial flash floods during rainfall events. Section 3 defines the key elements and actors involved in an effective EWS. Section 4 reviews different EWS architectures for pluvial flash floods implemented worldwide. It was identified that the reviewed projects did not follow guidelines to design early warning systems, neglecting important aspects that must be taken into account in their implementation. Therefore, this manuscript proposes a basic structure for an effective EWS for pluvial flash floods that guarantees the forecasting process and alerts dissemination during rainfall events.Administrative Department of Science, Technology and Innovation of the presidency of the Republic of Colombia (COLCIENCIAS) #728.Acosta-Coll, M.; Ballester Merelo, FJ.; Martínez Peiró, MA.; De La Hoz-Franco, E. (2018). Real-time early warning system design for pluvial flash floods - A review. Sensors. 18(7). https://doi.org/10.3390/s18072255S187Kundzewicz, Z. W. (2002). Non-structural Flood Protection and Sustainability. Water International, 27(1), 3-13. doi:10.1080/02508060208686972Singh, P., Sinha, V. S. P., Vijhani, A., & Pahuja, N. (2018). Vulnerability assessment of urban road network from urban flood. International Journal of Disaster Risk Reduction, 28, 237-250. doi:10.1016/j.ijdrr.2018.03.017Birkmann, J., & von Teichman, K. (2010). Integrating disaster risk reduction and climate change adaptation: key challenges—scales, knowledge, and norms. Sustainability Science, 5(2), 171-184. doi:10.1007/s11625-010-0108-yEmerging Challenges for Early Warning Systems in context of Climate Change and Urbanizationhttp://www.preventionweb.net/ files/15689_ewsincontextofccandurbanization.pdfChaumillon, E., Bertin, X., Fortunato, A. B., Bajo, M., Schneider, J.-L., Dezileau, L., … Pedreros, R. (2017). Storm-induced marine flooding: Lessons from a multidisciplinary approach. Earth-Science Reviews, 165, 151-184. doi:10.1016/j.earscirev.2016.12.005Alfieri, L., Cohen, S., Galantowicz, J., Schumann, G. J.-P., Trigg, M. A., Zsoter, E., … Salamon, P. (2018). A global network for operational flood risk reduction. Environmental Science & Policy, 84, 149-158. doi:10.1016/j.envsci.2018.03.014Maggioni, V., & Massari, C. (2018). On the performance of satellite precipitation products in riverine flood modeling: A review. Journal of Hydrology, 558, 214-224. doi:10.1016/j.jhydrol.2018.01.039Jiang, Y., Zevenbergen, C., & Ma, Y. (2018). Urban pluvial flooding and stormwater management: A contemporary review of China’s challenges and «sponge cities» strategy. Environmental Science & Policy, 80, 132-143. doi:10.1016/j.envsci.2017.11.016Veldhuis, J. A. E. (2011). How the choice of flood damage metrics influences urban flood risk assessment. Journal of Flood Risk Management, 4(4), 281-287. doi:10.1111/j.1753-318x.2011.01112.xGlobal Approach to Address Flash Floodshttp://www.hrc-lab.org/publicbenefit/downloads/wmo-flashflood.pdfChen, Y., Zhou, H., Zhang, H., Du, G., & Zhou, J. (2015). Urban flood risk warning under rapid urbanization. Environmental Research, 139, 3-10. doi:10.1016/j.envres.2015.02.028Guerreiro, S., Glenis, V., Dawson, R., & Kilsby, C. (2017). Pluvial Flooding in European Cities—A Continental Approach to Urban Flood Modelling. Water, 9(4), 296. doi:10.3390/w9040296Bhattarai, R., Yoshimura, K., Seto, S., Nakamura, S., & Oki, T. (2016). Statistical model for economic damage from pluvial floods in Japan using rainfall data and socioeconomic parameters. Natural Hazards and Earth System Sciences, 16(5), 1063-1077. doi:10.5194/nhess-16-1063-2016Acosta-Coll, M., Ballester-Merelo, F., & Martínez-Peiró, M. (2018). Early warning system for detection of urban pluvial flooding hazard levels in an ungauged basin. Natural Hazards, 92(2), 1237-1265. doi:10.1007/s11069-018-3249-4Yin, J., Ye, M., Yin, Z., & Xu, S. (2014). A review of advances in urban flood risk analysis over China. Stochastic Environmental Research and Risk Assessment, 29(3), 1063-1070. doi:10.1007/s00477-014-0939-7Azam, M., Kim, H. S., & Maeng, S. J. (2017). Development of flood alert application in Mushim stream watershed Korea. International Journal of Disaster Risk Reduction, 21, 11-26. doi:10.1016/j.ijdrr.2016.11.008Creutin, J. D., Borga, M., Gruntfest, E., Lutoff, C., Zoccatelli, D., & Ruin, I. (2013). A space and time framework for analyzing human anticipation of flash floods. Journal of Hydrology, 482, 14-24. doi:10.1016/j.jhydrol.2012.11.009Yin, J., Yu, D., Yin, Z., Liu, M., & He, Q. (2016). Evaluating the impact and risk of pluvial flash flood on intra-urban road network: A case study in the city center of Shanghai, China. Journal of Hydrology, 537, 138-145. doi:10.1016/j.jhydrol.2016.03.037UNISDR Terminology on Disaster Risk Reductionhttps://www.unisdr.org/we/inform/publications/657Einfalt, T., Hatzfeld, F., Wagner, A., Seltmann, J., Castro, D., & Frerichs, S. (2009). URBAS: forecasting and management of flash floods in urban areas. Urban Water Journal, 6(5), 369-374. doi:10.1080/15730620902934819Lam, R. P. K., Leung, L. P., Balsari, S., Hsiao, K., Newnham, E., Patrick, K., … Leaning, J. (2017). Urban disaster preparedness of Hong Kong residents: A territory-wide survey. International Journal of Disaster Risk Reduction, 23, 62-69. doi:10.1016/j.ijdrr.2017.04.008Bouwer, L. M., Papyrakis, E., Poussin, J., Pfurtscheller, C., & Thieken, A. H. (2014). The Costing of Measures for Natural Hazard Mitigation in Europe. Natural Hazards Review, 15(4), 04014010. doi:10.1061/(asce)nh.1527-6996.0000133Praskievicz, S., & Chang, H. (2009). A review of hydrological modelling of basin-scale climate change and urban development impacts. Progress in Physical Geography: Earth and Environment, 33(5), 650-671. doi:10.1177/0309133309348098Hunt, A., & Watkiss, P. (2010). Climate change impacts and adaptation in cities: a review of the literature. Climatic Change, 104(1), 13-49. doi:10.1007/s10584-010-9975-6Kundzewicz, Z. W., Kanae, S., Seneviratne, S. I., Handmer, J., Nicholls, N., Peduzzi, P., … Sherstyukov, B. (2013). Flood risk and climate change: global and regional perspectives. Hydrological Sciences Journal, 59(1), 1-28. doi:10.1080/02626667.2013.857411You, Q., Kang, S., Aguilar, E., Pepin, N., Flügel, W.-A., Yan, Y., … Huang, J. (2010). Changes in daily climate extremes in China and their connection to the large scale atmospheric circulation during 1961–2003. Climate Dynamics, 36(11-12), 2399-2417. doi:10.1007/s00382-009-0735-0Miller, J. D., & Hutchins, M. (2017). The impacts of urbanisation and climate change on urban flooding and urban water quality: A review of the evidence concerning the United Kingdom. Journal of Hydrology: Regional Studies, 12, 345-362. doi:10.1016/j.ejrh.2017.06.006Borga, M., Anagnostou, E. N., Blöschl, G., & Creutin, J.-D. (2011). Flash flood forecasting, warning and risk management: the HYDRATE project. Environmental Science & Policy, 14(7), 834-844. doi:10.1016/j.envsci.2011.05.017Grillakis, M. G., Koutroulis, A. G., Komma, J., Tsanis, I. K., Wagner, W., & Blöschl, G. (2016). Initial soil moisture effects on flash flood generation – A comparison between basins of contrasting hydro-climatic conditions. Journal of Hydrology, 541, 206-217. doi:10.1016/j.jhydrol.2016.03.007Zhang, J., Yu, Z., Yu, T., Si, J., Feng, Q., & Cao, S. (2018). Transforming flash floods into resources in arid China. Land Use Policy, 76, 746-753. doi:10.1016/j.landusepol.2018.03.002Spiekermann, R., Kienberger, S., Norton, J., Briones, F., & Weichselgartner, J. (2015). The Disaster-Knowledge Matrix – Reframing and evaluating the knowledge challenges in disaster risk reduction. International Journal of Disaster Risk Reduction, 13, 96-108. doi:10.1016/j.ijdrr.2015.05.002Weichselgartner, J., & Pigeon, P. (2015). The Role of Knowledge in Disaster Risk Reduction. International Journal of Disaster Risk Science, 6(2), 107-116. doi:10.1007/s13753-015-0052-7Hunt, D. P. (2003). The concept of knowledge and how to measure it. Journal of Intellectual Capital, 4(1), 100-113. doi:10.1108/14691930310455414Strengthening Capacities for Disaster Risk Reduction, A Primerhttps://www.preventionweb.net/files/globalplatform/entry_bg_paper~strengtheningcapacityfordrraprimerfullreport.pdfSurjan, A., Sharma, A., & Shaw, R. (2011). Chapter 2 Understanding Urban Resilience. Community, Environment and Disaster Risk Management, 17-45. doi:10.1108/s2040-7262(2011)0000006008Fakhruddin, S. H. M., Kawasaki, A., & Babel, M. S. (2015). Community responses to flood early warning system: Case study in Kaijuri Union, Bangladesh. International Journal of Disaster Risk Reduction, 14, 323-331. doi:10.1016/j.ijdrr.2015.08.004Balis, B., Kasztelnik, M., Bubak, M., Bartynski, T., Gubała, T., Nowakowski, P., & Broekhuijsen, J. (2011). The UrbanFlood Common Information Space for Early Warning Systems. Procedia Computer Science, 4, 96-105. doi:10.1016/j.procs.2011.04.011Krzhizhanovskaya, V. V., Shirshov, G. S., Melnikova, N. B., Belleman, R. G., Rusadi, F. I., Broekhuijsen, B. J., … Meijer, R. J. (2011). Flood early warning system: design, implementation and computational modules. Procedia Computer Science, 4, 106-115. doi:10.1016/j.procs.2011.04.012Chang, C. L., & Lin, T.-C. (2015). The role of organizational culture in the knowledge management process. Journal of Knowledge Management, 19(3), 433-455. doi:10.1108/jkm-08-2014-0353MARK, O., WEESAKUL, S., APIRUMANEKUL, C., AROONNET, S., & DJORDJEVIC, S. (2004). Potential and limitations of 1D modelling of urban flooding. Journal of Hydrology, 299(3-4), 284-299. doi:10.1016/s0022-1694(04)00373-7Henonin, J., Russo, B., Mark, O., & Gourbesville, P. (2013). Real-time urban flood forecasting and modelling – a state of the art. Journal of Hydroinformatics, 15(3), 717-736. doi:10.2166/hydro.2013.132Mayhorn, C. B., & McLaughlin, A. C. (2014). Warning the world of extreme events: A global perspective on risk communication for natural and technological disaster. Safety Science, 61, 43-50. doi:10.1016/j.ssci.2012.04.014Cools, J., Innocenti, D., & O’Brien, S. (2016). Lessons from flood early warning systems. Environmental Science & Policy, 58, 117-122. doi:10.1016/j.envsci.2016.01.006Plate, E. J. (2007). Early warning and flood forecasting for large rivers with the lower Mekong as example. Journal of Hydro-environment Research, 1(2), 80-94. doi:10.1016/j.jher.2007.10.002Altay, N., & Green, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475-493. doi:10.1016/j.ejor.2005.05.016Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., & Pappenberger, F. (2013). GloFAS – global ensemble streamflow forecasting and flood early warning. Hydrology and Earth System Sciences, 17(3), 1161-1175. doi:10.5194/hess-17-1161-2013Morss, R. E., Mulder, K. J., Lazo, J. K., & Demuth, J. L. (2016). How do people perceive, understand, and anticipate responding to flash flood risks and warnings? Results from a public survey in Boulder, Colorado, USA. Journal of Hydrology, 541, 649-664. doi:10.1016/j.jhydrol.2015.11.047Cama-Pinto, A., Acosta-Coll, M., Piñeres-Espitia, G., Caicedo-Ortiz, J., Zamora-Musa, R., & Sepulveda-Ojeda, J. (2016). Diseño de una red de sensores inalámbricos para la monitorización de inundaciones repentinas en la ciudad de Barranquilla, Colombia. Ingeniare. Revista chilena de ingeniería, 24(4), 581-599. doi:10.4067/s0718-33052016000400005Espitia, G. P. (2014). Plataformas tecnológicas aplicadas al monitoreo climático. Prospectiva, 11(2), 78. doi:10.15665/rp.v11i2.42Caicedo Ortiz, J. G. (2015). Modelo de despliegue de una WSN para la medición de las variables climáticas que causan fuertes precipitaciones. Prospectiva, 13(1), 106. doi:10.15665/rp.v13i1.365Marshall, J. S., & Palmer, W. M. K. (1948). THE DISTRIBUTION OF RAINDROPS WITH SIZE. Journal of Meteorology, 5(4), 165-166. doi:10.1175/1520-0469(1948)0052.0.co;2Liquid-Level Monitoring Using a Pressure Sensorhttp://www.ti.com/lit/an/snaa127/snaa127.pdfUltrasonic Transmitters vshttps://www.flo-corp.com/wp-content/uploads/2017/01/LTT1_UltrasonicTransmitters_GuidedWaveRadar_LevelMeasurement_whitepaper.pdfPanda, K. G., Agrawal, D., Nshimiyimana, A., & Hossain, A. (2016). Effects of environment on accuracy of ultrasonic sensor operates in millimetre range. Perspectives in Science, 8, 574-576. doi:10.1016/j.pisc.2016.06.024Saad, C., Mostafa, B., Ahmadi, E., & Abderrahmane, H. (2014). Comparative Performance Analysis of Wireless Communication Protocols for Intelligent Sensors and Their Applications. International Journal of Advanced Computer Science and Applications, 5(4). doi:10.14569/ijacsa.2014.050413FloodCitiSense: Early Warning Service for Urban Pluvial Floods for and by Citizens and City Authoritieshttp://www.iiasa.ac.at/web/home/research/researchPrograms/EcosystemsServicesandManagement/FloodCitiSense.htmlParker, D. J. (2017). Flood Warning Systems and Their Performance. Oxford Research Encyclopedia of Natural Hazard Science. doi:10.1093/acrefore/9780199389407.013.8

A service-oriented middleware for integrated management of crowdsourced and sensor data streams in disaster management

The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. As traditional sensor networks are error-prone and difficult to maintain, the study highlights the emerging role of “citizens as sensors” as a complementary data source to increase public awareness. To this end, an interoperable, reusable middleware for managing spatial, temporal, and thematic data using Sensor Web Enablement initiative services and a processing engine was designed, implemented, and deployed. The study found that its approach provided effective sensor data-stream access, publication, and filtering in dynamic scenarios such as disaster management, as well as it enables batch and stream management integration. Also, an interoperability analytics testing of a flood citizen observatory highlighted even variable data such as those provided by the crowd can be integrated with sensor data stream. Our approach, thus, offers a mean to improve near-real-time applications

Volunteered geographic information in natural hazard analysis : a systematic literature review of current approaches with a focus on preparedness and mitigation

With the rise of new technologies, citizens can contribute to scientific research via Web 2.0 applications for collecting and distributing geospatial data. Integrating local knowledge, personal experience and up-to-date geoinformation indicates a promising approach for the theoretical framework and the methods of natural hazard analysis. Our systematic literature review aims at identifying current research and directions for future research in terms of Volunteered Geographic Information (VGI) within natural hazard analysis. Focusing on both the preparedness and mitigation phase results in eleven articles from two literature databases. A qualitative analysis for in-depth information extraction reveals auspicious approaches regarding community engagement and data fusion, but also important research gaps. Mainly based in Europe and North America, the analysed studies deal primarily with floods and forest fires, applying geodata collected by trained citizens who are improving their knowledge and making their own interpretations. Yet, there is still a lack of common scientific terms and concepts. Future research can use these findings for the adaptation of scientific models of natural hazard analysis in order to enable the fusion of data from technical sensors and VGI. The development of such general methods shall contribute to establishing the user integration into various contexts, such as natural hazard analysis

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

The numbers of natural disaster events are continuously affecting human and the world economics. For coping with disaster, several sectors try to develop the frameworks, systems, technologies and so on. However, there are little researches focusing on the usage behavior of Information Technology (IT) for disaster risk management (DRM). Therefore, this study investigates the affecting factors on the intention to use IT for mitigating disaster’s impacts. This study conducted a systematic review with the academic researches during 2011-2018. Two important factors from the Technology Acceptance Model (TAM) and others are used in describing individual behavior. In order to investigate the potential factors, the technology platforms are divided into nine types. According to the findings, computer software such as GIS applications are frequently used for simulation and spatial data analysis. Social media is preferred among the first choices during disaster events in order to communicate about situations and damages. Finally, we found five major potential factors which are Perceived Usefulness (PU), Perceived Ease of Use (PEOU), information accessibility, social influence, and disaster knowledge. Among them, the most essential one of using IT for disaster management is PU, while PEOU and information accessibility are more important in the web platforms

Models of everywhere revisited: a technological perspective

The concept ‘models of everywhere’ was first introduced in the mid 2000s as a means of reasoning about the environmental science of a place, changing the nature of the underlying modelling process, from one in which general model structures are used to one in which modelling becomes a learning process about specific places, in particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere, models of everything and models at all times, being constantly re-evaluated against the most current evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities. However, the approach has, as yet, not been fully utilised or explored. This paper examines the concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the remaining research questions. The paper concludes by identifying the key elements of a research agenda that should underpin such experimentation and deployment