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Remote Sensing Based Model Induction for Drought Monitoring and Rainfall Estimation

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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

Droughts are natural phenomenon threatening many countries around the globe. In this work, we study regional drought in the northeastern area of Thailand. Since 1975 droughts in the northeast, especially Nakhon Ratchasima province in the south of this region, have occurred more frequently than the past with stronger intensity. We firstly investigate the relationship of regional drought to the cycle of El Nino Southern Oscillation (ENSO) and find that the cool phase of ENSO (or La Nina) shows positive effect to the increase of rainfall, whereas the warm phase (or El Nino) has no clear relationship to the decrease of rainfall in Nakhon Ratchasima province. We then further our study by inducing a model to monitor drought situation based on the historical remotely sensed data. Data in the past six years had been selected from both the excessive rain fall years due to the La Nina effect and the drought years with unclear cause. We also draw a model to estimate the amount of rainfall from the lagged two and three months of remotely sensed data. The drought monitoring model and the rainfall estimation model are built by the decision tree induction algorithm and the models’ accuracy tested with 10-fold cross validation are 68.6 % and 72.2 %, respectively.

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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). Remote Sensing Based Model Induction for Drought Monitoring and Rainfall Estimation. 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_28

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  • DOI: https://doi.org/10.1007/978-3-319-42111-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42110-0

  • Online ISBN: 978-3-319-42111-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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