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Babak Mohammadi

Doktorand

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An improved adaptive neuro-fuzzy inference system for hydrological drought prediction in Algeria

Författare

  • Mohammed Achite
  • Enes Gul
  • Nehal Elshaboury
  • Muhammad Jehanzaib
  • Babak Mohammadi
  • Ali Danandeh Mehr

Summary, in English

Drought has negative impacts on water resources, food security, soil degradation, desertification and agricultural productivity. The meteorological and hydrological droughts prediction using standardized precipitation/runoff indices (SPI/SRI) is crucial for effective water resource management. In this study, we suggest ANFISWCA, an adaptive neuro-fuzzy inference system (ANFIS) optimized by the water cycle algorithm (WCA), for hydrological drought forecasting in semi-arid regions of Algeria. The new model was used to predict SRI at 3-, 6-, 9-, and 12-month accumulation periods in the Wadi Mina basin, Algeria. The results of the model were assessed using four criteria; determination coefficient, mean absolute error, variance accounted for, and root mean square error, and compared with those of the standalone ANFIS model. The findings suggested that throughout the testing phase at all the sub-basins, the proposed hybrid model outperformed the conventional model for estimating drought. This study indicated that the WCA algorithm enhanced the ANFIS model's drought forecasting accuracy. The proposed model could be employed for forecasting drought at multi-timescales, deciding on remedial strategies for dealing with drought at study stations, and aiding in sustainable water resources management.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap

Publiceringsår

2023-10

Språk

Engelska

Publikation/Tidskrift/Serie

Physics and Chemistry of the Earth

Volym

131

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Oceanography, Hydrology, Water Resources
  • Water Engineering

Nyckelord

  • ANFIS
  • Hybrid model
  • Hydrological drought
  • Water cycle algorithm

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1474-7065