Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Science and Its Applications

ICCSA 2016: Computational Science and Its Applications -- ICCSA 2016 pp 332–344Cite as

Geo-Information Knowledge Base System for Drought Pattern Search

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9788))

Abstract

This paper presents a novel idea of utilizing a first-order logic technique to create, search and match for drought patterns in the specific area of interest. Drought patterns in this work have been drawn from the regression analysis using weekly time period and vegetation health index (VHI) obtained from the National Oceanic and Atmospheric Administration (NOAA) satellite data. To show the devised search facility, we use drought situations in the northeast provinces of Thailand as demonstrative cases. Drought trend of each province can be inferred from the percentage of provincial area that has the value of VHI below 35; the lower VHI value, the more severe drought. According to NOAA, this VHI threshold indicates moderate-to-high drought level. The proposed method is a kind of geo-information knowledge base system that allows users to search for drought pattern in some specific area at a particular time of the year. Moreover, the system also reports other area in the same region of study that shows similar drought trend. The drought trend is recognized from a regression coefficient in which the positive coefficient is the sign of increasing drought level, whereas the negative value implies the decrease of drought level.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Cohen, Y., Shoshany, M.: A national knowledge-based crop recognition in Mediterranean environment. Int. J. Appl. Earth Obs. Geoinf. 4, 75–87 (2002)

    Article  Google Scholar 

  2. Goyal, R., Jayasudha, T., Pandey, P., Devi, D.R., Rebecca, A., Sarma, M.M., Lakshmi, B.: Knowledge based system for satellite data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XL-8, 1233–1236 (2014)

    Article  Google Scholar 

  3. Kahya, O., Bayram, B., Reis, S.: Land cover classification with an expert system approach using Landsat ETM imagery: a case study of Trabzon. Environ. Monit. Assess. 160(1), 431–438 (2010)

    Article  Google Scholar 

  4. Kartikeyan, B., Majumder, K.L., Dasgupta, A.R.: An expert system for land cover classification. IEEE Trans. Geosci. Remote Sens. 33(1), 58–66 (1995)

    Article  Google Scholar 

  5. Kerdprasop, K., Kerdprasop, N.: Integrating inductive knowledge into the inference system of biomedical informatics. In: Kim, T.-h., Adeli, H., Cuzzocrea, A., Arslan, T., Zhang, Y., Ma, J., Chung, K.-i., Mariyam, S., Song, X. (eds.) DTA/BSBT 2011. CCIS, vol. 258, pp. 133–142. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  6. Kerdprasop, K., Kerdprasop, N.: Automatic knowledge acquisition tool to support intelligent manufacturing systems. Adv. Sci. Lett. 13, 199–202 (2012)

    Article  Google Scholar 

  7. Kerdprasop, N., Intharachatorn, K., Kerdprasop, K.: Prototyping an expert system shell with the logic-based approach. Int. J. Smart Home 7(4), 161–174 (2013)

    Google Scholar 

  8. Kerdprasop, N., Kerdprasop, K.: Autonomous integration of induced knowledge into expert system inference engine. In: IMECS 2011 – International MultiConference of Engineers and Computer Scientists, vol. 1, pp. 90–95 (2011)

    Google Scholar 

  9. Krapivin, V.F., Phillips, G.W.: A remote sensing-based expert system to study the Aral-Caspian aquageosystem water regime. Remote Sens. Environ. 75, 201–215 (2001)

    Article  Google Scholar 

  10. Lucas, R., Rowlands, A., Brown, A., Keyworth, S., Bunting, P.: Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping. ISPRS J. Photogramm. Remote Sens. 62, 165–185 (2007)

    Article  Google Scholar 

  11. McKeown, D.M.: The role of artificial intelligence in the integration of remotely sensed data with geographic information systems. Technical report CMU-CS-86-174, Computer Science Department, Carnegie Mellon University, USA (1986)

    Google Scholar 

  12. Shesham, S.: Integrating expert system and geographic information system for spatial decision making. Master Theses & Specialist Projects, Paper 1216, Western Kentucky University, U.S.A. (2012)

    Google Scholar 

  13. Shoshany, M.: Knowledge based expert systems in remote sensing tasks: quantifying gain from intelligent inference. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XXXVII, Part B7, 1085–1088 (2008)

    Google Scholar 

  14. Stefanov, W.L., Ramsey, M.S., Christensen, P.R.: Monitoring urban land cover change: an expert system approach to land cover classification of semiarid to arid urban centers. Remote Sens. Environ. 77, 173–185 (2001)

    Article  Google Scholar 

  15. Thorat, S.S., Rajendra, Y.D., Kale, K.V., Mehrotra, S.C.: Estimation of crop and forest areas using expert system based knowledge classifier approach for Aurangabad district. Int. J. Comput. Appl. 121, 43–46 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Data and Knowledge Engineering Research Unit, School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand

    Kittisak Kerdprasop & Nittaya Kerdprasop

Authors
  1. Kittisak Kerdprasop
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nittaya Kerdprasop
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nittaya Kerdprasop .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  5. Polytechnic University, Bari, Italy

    Carmelo M. Torre

  6. Monash University, Clayton, Victoria, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  9. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Kerdprasop, K., Kerdprasop, N. (2016). Geo-Information Knowledge Base System for Drought Pattern Search. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9788. Springer, Cham. https://doi.org/10.1007/978-3-319-42111-7_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42111-7_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42110-0

  • Online ISBN: 978-3-319-42111-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation