Hydrological drought prediction based on hybrid extreme learning machine: Wadi Mina Basin case study, Algeria

Mohammed Achite, Okan Mert Katipoğlu, Muhammad Jehanzaib*, Nehal Elshaboury, Veysi Kartal, Shoaib Ali

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
2 Downloads (Pure)

Abstract

Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM’s fast-learning ability and discrete wavelet transform’s ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria.

Original languageEnglish
Article number1447
Number of pages23
JournalAtmosphere
Volume14
Issue number9
DOIs
Publication statusPublished - 17 Sept 2023
Externally publishedYes

Keywords

  • Algeria
  • drought indices
  • extreme learning machine
  • standardized runoff index
  • wavelet transform

ASJC Scopus subject areas

  • Environmental Science (miscellaneous)

Fingerprint

Dive into the research topics of 'Hydrological drought prediction based on hybrid extreme learning machine: Wadi Mina Basin case study, Algeria'. Together they form a unique fingerprint.

Cite this