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

Author(s): Vitali Diaz; Gerald A. Corzo Perez; Henny A. J. Van Lanen; Dimitri Solomatine

Linked Author(s): Dimitri Solomatine, Gerald Augusto Corzo Perez

Keywords: Contiguous Drought Area (CDA) analysis; Drought monitoring; Drought tracking; Machine Learning

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, it is still a challenge. This study proposes a method to monitor drought by tracking its spatial extent. In this paper, the methodology to build drought trajectories is introduced. The concept of machine learning (ML) is mentioned as the target objective, however, not presented yet. 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. 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.


Year: 2018

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