Interactive Landslide Simulator: A Tool for Landslide Risk Assessment and Communication

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 481)

Abstract

Understanding landslide risks is important for people living in hilly areas in India. A promising way of communicating landslide risks is via simulation tools, where these tools integrate both human factors (e.g., public investments to mitigate landslides) and environmental factors (e.g., spatial geology and rainfall). In this paper, we develop an interactive simulation model on landslide risks and use it to design a web-based Interactive Landslide Simulator (ILS) microworld. The ILS microworld is based on the assumption that landslides occur due to both environmental factors (spatial geology and rainfall) as well as human factors (lack of monetary investments to mitigate landslides). We run a lab-based experiment involving human participants performing in ILS and we show that the ILS performance helps improve public understanding of landslide risks. Overall, we propose ILS to be an effective tool for doing what-if analyses by policymakers and for educating public about landslide risks.

Keywords

Early warning systems Interactive landslide simulator (ILS) Landslide risk communication Feedback Learning 

Notes

Acknowledgments

This research was partially supported by Thapar University, Patiala and Indian Institute of Technology, Mandi, India. The authors thank Akanksha Jain and Sushmita Negi, Centre for Converging Technologies, University of Rajasthan for their contribution in collection of human data.

References

  1. 1.
    Basher, R.: Global early warning systems for natural hazards: systematic and people-centred. Philos. Trans. R. Soc. London A Math. Phys. Eng. Sci. 364(1845), 2167–2182 (2006)CrossRefGoogle Scholar
  2. 2.
    Meissen, U., Voisard, A.: Increasing the effectiveness of early warning via context-aware alerting. In: Proceedings of the 5th International Conference, on Information Systems for Crisis Response and Management (ISCRAM), pp. 431–440 (2008)Google Scholar
  3. 3.
    Villagran de Leon, J.C., Pruessner, I., Breedlove, H.: Alert and Warning Frameworks in the Context of Early Warning Systems (2013)Google Scholar
  4. 4.
    Chaturvedi, P., Dutt V.: Evaluating the public perceptions of landslide risks in the Himalayan Mandi town. Accepted for Presentation in the 2015 Human Factor & Ergonomics Society (HFES) Annual Meeting, L.A (2015)Google Scholar
  5. 5.
    Oven, K.: Landscape, livelihoods and risk: community vulnerability to landslides in Nepal. Doctoral Dissertation, Durham University (2009)Google Scholar
  6. 6.
    Wanasolo, I.: Assessing and Mapping People’s Perceptions of Vulnerability to Landslides in Bududa, Uganda (2012)Google Scholar
  7. 7.
    Dutt, V., Gonzalez, C.: Why do we want to delay actions on climate change? Effects of probability and timing of climate consequences. J. Behav. Decis. Mak. 25(2), 154–164 (2012)CrossRefGoogle Scholar
  8. 8.
    Dutt, V., Gonzalez, C.: Decisions from experience reduce misconceptions about climate change. J. Environ. Psychol. 32(1), 19–29 (2012). doi:10.1016/j.jenvp.2011.10.003 CrossRefGoogle Scholar
  9. 9.
    Knutti, R., Joos, F., Müller, S.A., Plattner, G.K., Stocker, T.F.: Probabilistic climate change projections for CO2 stabilization profiles. Geophys. Res. Lett. 32(20) (2005)Google Scholar
  10. 10.
    Wagner, K.: Mental models of flash floods and landslides. Risk Anal. 27(3), 671–682 (2007)CrossRefGoogle Scholar
  11. 11.
    Baumeister, R.F., Vohs, K.D., Tice, D.M.: The strength model of self-control. Curr. Dir. Psychol. Sci. 16(6), 351–355 (2007)CrossRefGoogle Scholar
  12. 12.
    Finucane, M.L., Alhakami, A., Slovic, P., Johnson, S.M.: The affect heuristic in judgments of risks and benefits. J. Behav. Decis. Mak. 13(1), 1–17 (2000)CrossRefGoogle Scholar
  13. 13.
    Hasson, R., Löfgren, Å., Visser, M.: Climate Change Disaster Management: Mitigation and Adaptation in a Public Goods Framework, No. 178 (2010)Google Scholar
  14. 14.
    Geosciences Group: Experimental Landslide Early Warning System for Rainfall Triggered Landslides along Rishikesh-Badrinath, Rishikesh-Uttarkashi-Gaumukh, Chamoli-Okhimath, Rudraprayag-Kedarnath and Pithoragarh-Malpa route corridors, Uttarakhand: Approach document. http://bhuvan-noeda.nrsc.gov.in/disaster/disaster/tools/landslide/doc/landslide_warning.pdf (2015). Accessed 10 Mar 2016
  15. 15.
    Parkash, S.: Historical records of socio-economically significant landslides in India. J. South Asia Disaster Stud. 4(2), 177–204 (2011)Google Scholar
  16. 16.
    Foss, B.A., Eikaas, T.I.: Game play in engineering education: concept and experimental results. Int. J. Eng. Educ. 22(5), 1043–1052 (2006)Google Scholar
  17. 17.
    Gonzalez, C., Vanyukov, P., Martin, M.K.: The use of microworlds to study dynamic decision making. Comput. Hum. Behav. 21, 273–286 (2005)CrossRefGoogle Scholar
  18. 18.
    Paich, M., Sterman, J.D.: Boom, bust, and failures to learn in experimental markets. Manage. Sci. 39(12), 1439–1458 (1993)CrossRefGoogle Scholar
  19. 19.
    Sterman, J.D.: Teaching takes off, flight simulators for management education: ‘‘The Beer Game’’. http://web.mit.edu/jsterman/www/SDG/beergame.html (2011). Accessed 18 Jan 2011
  20. 20.
    Edwards, W.: Dynamic decision theory and probabilistic information processing. Hum. Factors 4, 59–73 (1962)Google Scholar
  21. 21.
    Sterman, J.D.: Business dynamics: systems thinking and modeling for a complex world. McGraw Hill, Cambridge (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Defence Terrain Research Laboratory (DTRL)DelhiIndia
  2. 2.Applied Cognitive Science LaboratoryIndian Institute of Technology (IIT) MandiMandiIndia
  3. 3.Thapar UniversityPatialaIndia

Personalised recommendations