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Geo-Information Knowledge Base System for Drought Pattern Search

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Computational Science and Its Applications -- ICCSA 2016 (ICCSA 2016)

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

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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.

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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
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  2. Nittaya Kerdprasop
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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

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© 2016 Springer International Publishing Switzerland

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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

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42110-0

  • Online ISBN: 978-3-319-42111-7

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