Skip to main content

Part of the book series: Studies in Computational Intelligence ((SCI,volume 209))

  • 581 Accesses

Introduction

Recently, there are a lot of researches concerned with natural disaster simulation by using data gathering useful information from sensors and climate management systems. However, most of simulations are based on high specs computers since the simulation is conducted with numerical and quantitative method[1][2][4][7]. By using huge data such as climate, temperature, and other physical status about degree of altitude, the simulation gives a rigorous result of analysis even though the situation and circumstance is complex like property of the atmosphere. Further, they are also developed for specialist but naive users and novices.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agell, N., Aguado, C.J.: A hybrid qualitative-quantitative classification technique applied to aid marketing decisions. In: The proceedings of 11th International Workshop on Qualitative Reasoning (2001)

    Google Scholar 

  2. Bredeweg, B., Winkels, R.: Qualitative models in interactive learning environments. Interactive Learning Environments 5 (1998)

    Google Scholar 

  3. Chen, S.A., Wang, J., Yang, C.S.: Constructing internet futures exchange for teach-ing derivatives trading in financial markets. In: The proceedings of International Conference on Computers in Education, vol. 2, pp. 1392–1395 (2002)

    Google Scholar 

  4. Forbus, K.D.: Helping children become qualitative modelers. Journal of the Japanese Society for Artificial Intelligence 17(4) (2002)

    Google Scholar 

  5. Hata, S., Ohkawa, T., Komoda, N.: Backward simulation method in qualitative simulation. IEEJ Transactions on Electronics, Information and Systems, Institute of Electrical Engineers of Japan 115-C(11) (1995)

    Google Scholar 

  6. Kuipers, B.: Qualitative Reasoning. The MIT Press, Cambridge (1994)

    Google Scholar 

  7. Matsuo, T., Ito, T., Shintani, T.: A qualitative/quantitative methods-based e-learning support system in economic education. In: The 19th National Conference on Artificial Intelligence (AAAI 2004) (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Matsuo, T., Hatano, N. (2009). Multiple Factors Based Qualitative Simulation for Flood Analysis. In: Lee, R., Ishii, N. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01203-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-01203-7_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01202-0

  • Online ISBN: 978-3-642-01203-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics