Abstract
Presently, the Internet of Things (IoT) and big data analytics technology offer enormous opportunities to disaster risk management services. Recent disaster risk management patterns involve disaster risk reduction to avoid future disaster risks, minimize current disastrous risks, resilience building, and disaster loss reduction. The main challenges in disaster management are communication within a disaster zone which is being disrupted. In this paper, the Internet of Things assisted disaster risk management framework (IOTDRMF) has been proposed for technical resources to communicate the emergency time and better visibility into reliable and prompt decision-making through observing, evaluating, and forecasting natural disasters. This IOTDRMF utilizes big data analytics to analyze disaster risk management build a kind of spatial data communication network infrastructure, making it a priority to establish rules, protocols, and knowledge sharing. The experimental results show the IOTDRMF, and big data analytics compensate for a weak communication network infrastructure and better decision-making to handle disaster risk management.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdel-Basset M, Mohamed R, Elhoseny M, Chang V (2020) Evaluation framework for smart disaster response systems in uncertainty environment. Mechanical Systems and Signal Processing 145:106941
Aggarwal A, Rani A, Sharma P, Kumar M, Shankar A, Alazab M (2020) Prediction of landsliding using univariate forecasting models. Internet Technol Lett. https://doi.org/10.1002/itl2.209
Ahmed SH, Lee S, Park J, Kim D, Rawat DB (2017, January). iDFR: Intelligent directional flooding-based routing protocols for underwater sensor networks. In: 2017 14th IEEE annual consumer communications & networking conference (CCNC). IEEE, pp 560–565
Al-Turjman F (2019a) Cognitive routing protocol for disaster-inspired internet of things. Futur Gener Comput Syst 92:1103–1115
Al-Turjman F (2019b) A rational data delivery framework for disaster-inspired internet of nano-things (IoNT) in practice. Clust Comput 22(1):1751–1763
Anbarasan M, Muthu B, Sivaparthipan CB, Sundarasekar R, Kadry S, Krishnamoorthy S, Dasel AA (2020) Detection of flood disaster system based on IoT, big data, and convolutional deep neural network. Comput Commun 150:150–157
Balamurugan S, Muthu BA, Peng SL, Wahab MHA (2020) Call for special issue papers: big data analytics for agricultural disaster management: deadline for manuscript submission: December 12, 2020. In: Big Data, vol 8, no 5, pp 450–451
Bhoi A, Pujari SP, Balabantaray RC (2020) A deep learning-based social media text analysis framework for disaster resource management. Soc Netw Anal Min 10(1):1–14
Chen RS, Li TS (2020) Identifying the multiplex network of collaborative decision-making in municipal disaster preparation. J US China Public Adm 17(5):192–202
Cumbane SP, Gidófalvi G (2019) Review of big data and processing frameworks for disaster response applications. ISPRS Int J Geo Inf 8(9):387
Edjossan-Sossou AM, Galvez D, Deck O, Al Heib M, Verdel T, Dupont L, Morel L (2020). Sustainable risk management strategy selection using a fuzzy multi-criteria decision approach. Int J Disast Risk ReducInt J Disast Risk Reduc 45:101474
Hannan A, Arshad S, Azam MA, Loo J, Ahmed SH, Majeed MF, Shah SC (2018) Disaster management system aided by named data network of things: architecture, design, and analysis. Sensors 18(8):2431
Hu S, Fu Z, Jackson Samuel RD, Anandhan P (2021) Application of active remote sensing in confirmation rights and identification of mortgage supply-demand subjects of rural land in Guangdong Province. Eur J Remote Sens 54(sup2):396–404
Kara ME, Fırat SÜO, Ghadge A (2020). A data mining-based framework for supply chain risk management. Comput Indu Eng 139:105570
Kumar JS, Kumar S, Choksi M, Zaveri MA (2020) Collaborative data acquisition and processing for post disaster management and surveillance related tasks using UAV-based IoT cloud. Int J Ad Hoc Ubiquitous Comput 34(4):216–232
Ma C, Yang Y, Ma C (2019) Mobility-based sinknode-aided routing in disaster network under the background of big data. Clust Comput 22(5):11583–11590
Malekloo A, Ozer E, Al-Turjman F (2020) Combination of GIS and SHM in prognosis and diagnosis of bridges in earthquake-prone locations. In: Smart grid in IoT-enabled spaces: the road to intelligence in power, vol 139
Manogaran G, Lopez D (2018) Spatial cumulative sum algorithm with big data analytics for climate change detection. Comput Electr Eng 65:207–221
Manogaran G, Lopez D, Chilamkurti N (2018) In-Mapper combiner based MapReduce algorithm for processing of big climate data. Futur Gener Comput Syst 86:433–445
McDonald F, Horwell CJ, Wecker R, Dominelli L, Loh M, Kamanyire R, Ugarte C (2020) Facemask use for community protection from air pollution disasters: An ethical overview and framework to guide agency decision making. Int J Disaster Risk Reduct 43:101376
Meechang K, Leelawat N, Tang J, Kodaka A, Chintanapakdee C (2020) The acceptance of using information technology for disaster risk management: a systematic review. Eng. J.s 24(4):111–132
Nespeca V, Comes T, Meesters K, Brazier F (2020) Towards coordinated self-organization: an actor-centered framework for the design of disaster management information systems. Int J Disast Risk Reduc 51:101887
Pandey P, Litoriya R (2019) Elderly care through unusual behavior detection: a disaster management approach using IoT and intelligence. IBM J Res Dev 64(1/2):15–21
Rhoades AM, Jones AD, Srivastava A, Huang H, O’Brien TA, Patricola CM, Zhou Y (2020) The shifting scales of western US landfalling atmospheric rivers under climate change. Geophys Res Lett 47(17):089096
Shah SA, Seker DZ, Hameed S, Draheim D (2019) The rising role of big data analytics and IoT in disaster management: recent advances, taxonomy and prospects. IEEE Access 7:54595–54614
Srivastava AK, Grotjahn R, Ullrich PA (2019) A Multimodel technique for estimating future changes in extreme precipitation. AGUFM 2019:A51Q-2832
Srivastava AK (2017) Decadal predictability in climate models with and without interactive ocean dynamics (Doctoral dissertation, George Mason University)
Sun AY, Scanlon BR (2019) How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions. Environ Res Lett 14(7):073001
Ulil AMR, Sukaridhoto S, Tjahjono A, Basuki DK (2019) The vehicle as a mobile sensor network base iot and big data for pothole detection caused by flood disaster. E&ES 239(1):012034
Wang J, Meyer MC, Wu Y, Wang YU (2019) Maximum data-resolution efficiency for fog-computing supported spatial big data processing in disaster scenarios. IEEE Trans Parallel Distrib Syst 30(8):1826–1842
Author information
Authors and Affiliations
Contributions
Heqing Huang, BalaAnand Muthu contributed to conception and design of study. Sivaparthipan C.B was involved in acquisition of data. Heqing Huang contributed to analysis and/or interpretation of data. Li Zhou was involved in drafting the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by Vicente Garcia Diaz.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhou, L., Huang, H., Muthu, B. et al. Design of Internet of Things and big data analytics-based disaster risk management. Soft Comput 25, 12415–12427 (2021). https://doi.org/10.1007/s00500-021-05953-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-021-05953-5