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Flooding disaster resilience information framework for smart and connected communities

  • Sathish A. P. KumarEmail author
  • Shaowu Bao
  • Vivek Singh
  • Jason Hallstrom
Original Article
  • 19 Downloads

Abstract

This paper presents the research challenges of designing a combined physical sensor- and social sensor-based information framework to collect heterogeneous flooding disaster data, and then to fuse those data and generate actionable understandings. Our overall objective is to improve the response preparedness of critical infrastructures, contributing to the goal of smart and connected communities. We propose methods to model physical and social sensors, and open demographic data integration with regional knowledge, and to leverage these fused data for understanding impending events and conditions deleterious to lives and properties. In addition, the proposed system will predict the disaster events and provide knowledge-based recommendations to inform emergency management personnel to enable the resilience of the smart and connected communities. Preliminary experiments for the framework are promising. Further work is needed to validate the framework in collaboration with the local emergency managers.

Keywords

Sensors Flooding disaster resilience Smart and connected communities Emergency management Flood vulnerability index 

Notes

Acknowledgements

The work is partially supported by the National Science Foundation grant CNS-1763294. Authors would also like to acknowledge the support of Dr. Dong Wang, Dr. Brian Xu, and Mr. Ike Vayansky for their help in generating methodology diagram and experimental results. Authors would also like to thank Dr. Zhenlong Li for providing the social media data for analysis.

References

  1. 1.
    Adi A, Etzion O (2004) Amit—the situation manager. VLDB J 13:177–203CrossRefzbMATHGoogle Scholar
  2. 2.
    Anselin L, Syabri I, Kho Y (2005) GeoDa: an introduction to spatial data analysis. Geogr Anal 38:5–22CrossRefGoogle Scholar
  3. 3.
    Arctur D, Zeiler M (2004) Designing geodatabases: case studies in GIS data modeling. ESRI Press, RedlandsGoogle Scholar
  4. 4.
    Baheti R, Gill H (2011) Cyber-physical systems. In: The impact of control technology, pp 161–166Google Scholar
  5. 5.
    Bansal N, Koudas N (2007) Blogscope: spatio-temporal analysis of the blogosphere. In: Proceedings of the 16th international conference on World Wide Web, pp 1269–1270Google Scholar
  6. 6.
    Balica S, Wright NG (2009) A network of knowledge on applying an indicator-based methodology for minimizing flood vulnerability. Hydrol Process 23:2983–2986CrossRefGoogle Scholar
  7. 7.
    Bao S, Yu Z, Xu J, Yan T, Pietrafesa L, Gayes P (2016) An easy to implement method to couple weather research and forecast (WRF) with other geophysical models and its application on simulating a northwest pacific typhoon. Environ Model SoftwGoogle Scholar
  8. 8.
    Booij N, Holthuijsen LH, Ris RC (1996) The “SWAN” wave model for shallow water. In: Coast engineering proceedingGoogle Scholar
  9. 9.
    Beach A, Gartrell M, Xing X, Han R, Lv Q, Mishra S, Seada K (2010) Fusing mobile, sensor, and social data to fully enable context-aware computing. In: Proceedings of the eleventh workshop on mobile computing systems & applications, pp 60–65Google Scholar
  10. 10.
    Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2:1–8CrossRefGoogle Scholar
  11. 11.
    Chae H, Kim DH, Jeong D, In H (2006) A situation aware personalization in ubiquitous mobile computing environments. In: Emerging directions in embedded and ubiquitous computing, pp 213–223Google Scholar
  12. 12.
    Daniel F, Casati F, Soi S, Fox J, Zancarli D, Shan MC (2009) Hosted universal integration on the web: the mashart platform. In: Service-oriented computing, pp 647–648Google Scholar
  13. 13.
    Delft, Hydraulics (2006) Delft3D-FLOW user manual. Delft, the NetherlandsGoogle Scholar
  14. 14.
    Dey AK, Abowd GD, Salber D (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum Comput Interact 16:97–166CrossRefGoogle Scholar
  15. 15.
    Feng YH, Teng TH, Tan AH (2009) Modelling situation awareness for context-aware decision support. Expert Syst Appl 36:455–463CrossRefGoogle Scholar
  16. 16.
    Fuchs S, Kuhlicke C, Meyer V (2011) Editorial for the special issue: vulnerability to natural hazards—the challenge of integration. Nat Hazards 58:609–619CrossRefGoogle Scholar
  17. 17.
    Gao M, Singh VK, Jain R (2012) EventShop: From heterogeneous web streams to personalized situation detection and control. In: Proceedings of the ACM international conference on web scienceGoogle Scholar
  18. 18.
    Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2008) Detecting influenza epidemics using search engine query data. Nature 457:1012–1014CrossRefGoogle Scholar
  19. 19.
    Girolami M, Lenzi S, Furfari F, Chessa S (2008) Sail: A sensor abstraction and integration layer for context awareness. In: Software engineering and advanced applications, 2008. SEAA’08. 34th Euromicro conference, pp 374–381Google Scholar
  20. 20.
    Goodchild MF, Parks BO, Steyaert LT (1993) Environmental modeling with GIS. Oxford University Press, New YorkGoogle Scholar
  21. 21.
    Gray J, Liu DT, Nieto-Santisteban M, Szalay A, DeWitt DJ, Heber G (2005) Scientific data management in the coming decade. ACM SIGMOD Rec 34:34–41CrossRefGoogle Scholar
  22. 22.
    Harville DA (2008) Matrix algebra from a statistician’s perspective. Springer, BerlinzbMATHGoogle Scholar
  23. 23.
    Henricksen K, Indulska J, McFadden T, Balasubramaniam S (2005) Middleware for distributed context-aware systems. In: On the move to meaningful internet systems 2005: CoopIS, DOA, and ODBASE, pp 846–863Google Scholar
  24. 24.
    Hore B, Jafarpour H, Jain R, Ji S, Massaguer D, Mehrotra S, Venkatasubramanian N, Westermann U (2007) Design and implementation of a middleware for sentient spaces. Intell Secur Inf IEEE 2007:137–144Google Scholar
  25. 25.
    Hufschmidt G (2011) A comparative analysis of several vulnerability concepts. Nat Hazards 58:621–643CrossRefGoogle Scholar
  26. 26.
    Jadhav A, Purohit H, Kapanipathi P, Ananthram P, Ranabahu A, Nguyen V, Mendes PN, Smith AG, Cooney M, Sheth A (2010) Twitris 2.0: Semantically empowered system for understanding perceptions from social data. In: Proceedings of the semantic web challenge 2010Google Scholar
  27. 27.
    Jakobson JB, Lewis L (2006) A framework of cognitive situation modeling and recognition. In: Military communications conference, 2006. MILCOM 2006. IEEE, 2006, pp 1–7Google Scholar
  28. 28.
    Jia L, Lin L, Wei C (2009) “The research and application on streaming data of GIS data mining,” in Database Technology and Applications. First Int Workshop 2009:209–212Google Scholar
  29. 29.
    Jones CB, Purves R, Ruas A, Sanderson M, Sester M, Van Kreveld M, Weibel R (2002) Spatial information retrieval and geographical ontologies an overview of the SPIRIT project. In: Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval, 2002, pp 387–388Google Scholar
  30. 30.
    Kallenberg O (2002) Foundations of modern probability. Springer, BerlinCrossRefzbMATHGoogle Scholar
  31. 31.
    Kulldorff M (2001) Prospective time periodic geographical disease surveillance using a scan statistic. J R Stat Soc Ser A (Stat Soc) 164:61–72MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation. Wiley, OxfordGoogle Scholar
  33. 33.
    Loke SW (2004) Representing and reasoning with situations for context-aware pervasive computing: a logic programming perspective. Knowl Eng Rev 19:213–234CrossRefGoogle Scholar
  34. 34.
    Mitchell T (2005) Web mapping illustrated: using open source GIS toolkits. O’Reilly Media, NewtonGoogle Scholar
  35. 35.
    Nazari Shirehjini A (2006) Situation modelling: a domain analysis and user study. In: Intelligent environments, 2006. IE 06. 2nd IET international conference on, 2006, pp 193–199Google Scholar
  36. 36.
    Oloufa AA, Ikeda M, Oda H (2003) Situational awareness of construction equipment using GPS, wireless and web technologies. Autom Constr 12:737–748CrossRefGoogle Scholar
  37. 37.
    Popovich VV, Pankin AV, Voronin MN, Sokolova LA (2006) Intelligent situation awareness on a GIS basis. In: Military communications conference, 2006. IEEE, pp 1–7Google Scholar
  38. 38.
    Rao B, Minakakis L (2003) Evolution of mobile location-based services. Commun ACM 46:61–65CrossRefGoogle Scholar
  39. 39.
    Ratkiewicz J, Michael C, Mark M, Bruno G, Snehal P, Alessandro F, Filippo M (2011) Truthy: mapping the spread of astroturf in microblog streams. In: Proceedings of the 20th international conference companion on World Wide Web, ACM, pp 249–252Google Scholar
  40. 40.
    Roman R, Lopez J, Gritzalis S (2008) Situation awareness mechanisms for wireless sensor networks. Commun Mag IEEE 46:102–107CrossRefGoogle Scholar
  41. 41.
    Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes twitter users: Real-time event detection by social sensors. In: Proceedings of the 19th international conference on World Wide Web, 2010, pp 851–860Google Scholar
  42. 42.
    Khoukhi L, Cherkaoui S, Gaïti D (2009) Managing rescue and relief operations using wireless mobile ad hoc technology, the best way?. IEEE LCN On-MOVE, SwitzerlandCrossRefGoogle Scholar
  43. 43.
    Xu J, Wang S, Su S, Kumar SAP, Wu C (2016) Latent interest and topic mining on user-item bipartite networks. In: 2016 IEEE international conference on services computing (SCC), pp 778–781. IEEEGoogle Scholar
  44. 44.
    Xu B, Kumar SA (2015) Big data analytics framework for system health monitoring. In: Big data (BigData Congress), 2015 IEEE international congress on, pp 401–408. IEEEGoogle Scholar
  45. 45.
    Xu B, Kumar S (2015) A text mining classification framework and its experiments using aviation datasetsGoogle Scholar
  46. 46.
    Alampalayam SP, Kumar A (2004) Predictive security model using data mining. In: Global telecommunications conference, 2004. GLOBECOM’04. IEEE, vol 4, pp 2208–2212. IEEEGoogle Scholar
  47. 47.
    Alampalayam SP, Kumar A (2003) Security model for routing attacks in mobile ad hoc networks. In: Vehicular technology conference, 2003. VTC 2003-Fall. 2003 IEEE 58th, vol 3, pp 2122–2126. IEEEGoogle Scholar
  48. 48.
    Kumar S, Kumar A, Srinivasan S (2007) Statistical based intrusion detection framework using six sigma technique. IJCSNS 7(10):333Google Scholar
  49. 49.
    Alampalayam S, Natsheh EF (2008) Multivariate fuzzy analysis for Mobile ad hoc Network threat Detection. IJBDCN 4(3):1–30Google Scholar
  50. 50.
    Duran J, Kumar SAP (2011) CUDA based multi-objective parallel genetic algorithms: adapting evolutionary algorithms for document searches. In: Proceedings of the international conference on information and knowledge engineering (IKE). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), p 1Google Scholar
  51. 51.
    Ye J, Dobson S, McKeever S (2012) Situation identification techniques in pervasive computing: a review. Pervas Mob Comput 8(1):36–66CrossRefGoogle Scholar
  52. 52.
    Coronato A, De Pietro G (2013) Situation awareness in applications of ambient assisted living for cognitive impaired people. Mob Netw Appl 18(3):444–453CrossRefGoogle Scholar
  53. 53.
    McCarthy J (1963) Situations, actions, and causal laws. No. AI-MEMO-2. Stanford Univ CA Dept of Computer ScienceGoogle Scholar
  54. 54.
    Coronato A, De Florio V, Bakhouya M, Serugendo G (2012) Formal modeling of socio-technical collective adaptive systems. In: Self-adaptive and self-organizing systems workshops (SASOW), 2012 IEEE sixth international conference on, pp 187–192. IEEEGoogle Scholar
  55. 55.
    Endsley MR (2016) Designing for situation awareness: an approach to user-centered design. CRC, OxfordGoogle Scholar
  56. 56.
    Sun R, Zhang X, Mathews R (2006) Modeling meta-cognition in a cognitive architecture. Cognit Syst Res 7(4):327–338CrossRefGoogle Scholar
  57. 57.
    Subramanian K, Savitha R, Suresh S (2014) A complex-valued neuro-fuzzy inference system and its learning mechanism. Neurocomputing 123(10):110–120CrossRefGoogle Scholar
  58. 58.
    Asaithambi K, Nagoor Gani A (2016) Performance of self-organized and metacognitive neuro fuzzy system for traffic flow prediction. Int J Pure Appl Math 107(4):1025–1036CrossRefGoogle Scholar
  59. 59.
    Sateesh Babu G, Suresh S (2013) Meta-cognitive RBF network and its projection based learning algorithm for classification problems. Appl Soft Comput 13(1):654–666CrossRefGoogle Scholar
  60. 60.
    Yeh S, Lo J (2005) Assessing metacognitive knowledge in web-based CALL: a neural network approach. Comput Educ 44:97–113CrossRefGoogle Scholar
  61. 61.
    Anderson ML et al (2006) The metacognitive loop I: enhancing reinforcement learning with metacognitive monitoring and control for improved perturbation tolerance. J Exp Theor Artif Intell 18(3):387–411CrossRefGoogle Scholar
  62. 62.
    Subramanian K, Suresh K (2012) Human action recognition using meta-cognitive neuro-fuzzy inference system. Int J Neural Syst 22(06):1250028CrossRefGoogle Scholar
  63. 63.
    Savitha R, Suresh S, Sundararajan N (2012) Meta-cognitive learning in a fully complex-valued radial basis function neural network. Neural Comput 24(5):1297–1328MathSciNetCrossRefzbMATHGoogle Scholar
  64. 64.
    Paisner M, Perlis D, Cox MT (2013) Symbolic anomaly detection and assessment using growing neural gas. In: Proc. of 25th IEEE international conference on tools with artificial intelligence (ICTAI)Google Scholar
  65. 65.
    Löffler A, Klahold J, Rückert U (1999) Artificial neural networks for autonomous robot control: reflective navigation and adaptive sensor calibration. In: Proc. of the 6th intl conf on neural information processing, Perth, Australia, pp 667–672Google Scholar
  66. 66.
    Sateesh Babu G, Xiao-Li L, Suresh S (2016) Meta-cognitive regression neural network for function approximation: application to remaining useful life estimation. In: Proc. of 2016 international joint conference on neural networks (IJCNN)Google Scholar
  67. 67.
    Subramanian K, Das A, Suresh S, Savitha R (2014) A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm. Evolv Syst 5:219CrossRefGoogle Scholar
  68. 68.
    Masci J et al (2011) Stacked convolutional auto-encoders for hierarchical feature extraction. In: Proc. of international conference on artificial neural networks, pp 52–59Google Scholar
  69. 69.
    Sakurada M, Takehisa Y (2014) Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proc. of the 2nd workshop on machine learning for sensory data analysis, MLSDA, p 4Google Scholar
  70. 70.
    Sabokrou M et al (2015) Real-time anomaly detection and localization in crowded scenes. In: Proc. of IEEE conference on computer vision and pattern recognition workshops, pp 56–62Google Scholar
  71. 71.
    Feng Q et al (2016) Anomaly detection of spectrum in wireless communication via deep autoencoder. In: Proc. of international conference on computer science and its applications, pp 259–265Google Scholar
  72. 72.
    Shchepetkin AF, McWilliams JC (2005) The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Model 9:347–404CrossRefGoogle Scholar
  73. 73.
    Skamarock WC et al (2008) A description of the advanced research WRF Version 3. NCAR Technical Note NCAR/TN-475 + STR, p 113Google Scholar
  74. 74.
    Jongman B, Wagemaker J, Romero BR, De Perez EC (2015) Early flood detection for rapid humanitarian response: harnessing near real-time satellite and twitter signals. ISPRS Int J Geo-Inf 4:2246–2266CrossRefGoogle Scholar
  75. 75.
    Bao S, Gayes P, Pietrafesa L (2018) The need and rationale for a coastal flood risk index. In: OCEANS 2018 MTS/IEEE Charleston, IEEE, pp 1–4Google Scholar
  76. 76.
    Landsea C, Franklin J, Beven J (2015) The revised Atlantic hurricane dataset (HURDAT2). NOAA National Hurricane Center, USAGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sathish A. P. Kumar
    • 1
    Email author
  • Shaowu Bao
    • 2
  • Vivek Singh
    • 3
  • Jason Hallstrom
    • 4
  1. 1.Department of Computing SciencesCoastal Carolina UniversityConwayUSA
  2. 2.Department of Coastal and Marine System SciencesCoastal Carolina UniversityConwayUSA
  3. 3.School of CommunicationRutgers UniversityNew BrunswickUSA
  4. 4.College of Engineering and Computer ScienceFlorida Atlantic UniversityBoca RatonUSA

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