Intelligent Drought Tracking for its Use in Machine Learning: Implementation and First Results

6 pages•Published: September 20, 2018

Vitali Diaz, Gerald A. Corzo Perez, Henny A.J. Van Lanen and Dimitri Solomatine

Abstract

Due to the underlying characteristics of drought, monitoring of its spatio-temporal development is difficult. Last decades, drought monitoring have been increasingly developed, however, including its spatio-temporal dynamics is still a challenge. This study proposes a method to monitor drought by tracking its spatial extent. A methodology to build drought trajectories is introduced, which is put in the framework of machine learning (ML) for drought prediction. Steps for trajectories calculation are (1) spatial areas computation, (2) centroids localization, and (3) centroids linkage. The spatio- temporal analysis performed here follows the Contiguous Drought Area (CDA) analysis. The methodology is illustrated using grid data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor over India (1901-2013), as an example. Results show regions where drought with considerable coverage tend to occur, and suggest possible concurrent routes. Tracks of six of the most severe reported droughts were analysed. In all of them, areas overlap considerably over time, which suggest that drought remains in the same region for a period of time. Years with the largest drought areas were 2000 and 2002, which coincide with documented information presented. Further research is under development to setup the ML model to predict the track of drought.

Keyphrases: contiguous drought areas, drought monitoring, drought tracking, machine learning

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 601-606.

Links:	https://easychair.org/publications/paper/h7SC
	https://doi.org/10.29007/klgg

BibTeX entry

@inproceedings{HIC2018:Intelligent_Drought_Tracking_its,
  author    = {Vitali Diaz and Gerald A. Corzo Perez and Henny A.J. Van Lanen and Dimitri Solomatine},
  title     = {Intelligent Drought Tracking for its Use in Machine Learning: Implementation and First Results},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/h7SC},
  doi       = {10.29007/klgg},
  pages     = {601-606},
  year      = {2018}}

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