A machine learning model for drought tracking and forecasting using remote precipitation data and a standardized precipitation index from arid regions

Research output: Contribution to journalReview articleContributedpeer-review

Contributors

  • Moncef Bouaziz - , Chair of Remote Sensing, National Engineering School of Sfax (Author)
  • Emna Medhioub - , National Engineering School of Sfax, TUD Dresden University of Technology (Author)
  • Elmar Csaplovics - , Chair of Remote Sensing (Author)

Abstract

Drought is a catastrophe that impacts agriculture and causes economic and social damage. An effective monitoring and forecasting system is needed to assess the extent of droughts and to mitigate their effects at both spatial and temporal levels. To this end, we used a Standardized Precipitation Index (SPI) in various timescales to classify and track drought events based on CHIRPS rainfall data for the period between 1981 and 2019. Three models (M1, M2, M3) were then tested for annual drought prediction (SPI_12) using precipitation data and the lagged SPI as input variables. Extreme Learning Machine algorithms displayed rapid drought prediction, with high accuracy on different timescales (0.7–0.8 R2).

Details

Original languageEnglish
Article number104478
JournalJournal of arid environments
Volume189
Publication statusPublished - Jun 2021
Peer-reviewedYes

Keywords

Keywords

  • CHIRPS, Drought, Drought forecast, Extreme learning machine, Standardized precipitation index