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Babak Mohammadi

Doktorand

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Optimizing Extreme Learning Machine for Drought Forecasting : Water Cycle vs. Bacterial Foraging

Författare

  • Ali Danandeh Mehr
  • Rifat Tur
  • Mohammed Mustafa Alee
  • Enes Gul
  • Vahid Nourani
  • Shahrokh Shoaei
  • Babak Mohammadi

Summary, in English

Machine learning (ML) methods have shown noteworthy skill in recognizing environmental patterns. However, presence of weather noise associated with the chaotic characteristics of water cycle components restricts the capability of standalone ML models in the modeling of extreme climate events such as droughts. To tackle the problem, this article suggests two novel hybrid ML models based on combination of extreme learning machine (ELM) with water cycle algorithm (WCA) and bacterial foraging optimization (BFO). The new models, respectively called ELM-WCA and ELM-BFO, were applied to forecast standardized precipitation evapotranspiration index (SPEI) at Beypazari and Nallihan meteorological stations in Ankara province (Turkey). The performance of the proposed models was compared with those the standalone ELM considering root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and graphical plots. The forecasting results for three- and six-month accumulation periods showed that the ELM-WCA is superior to its counterparts. The NSE results of the SPEI-3 forecasting in the testing period proved that the ELM-WCA improved drought modeling accuracy of the standalone ELM up to 72% and 85% at Beypazari and Nallihan stations, respectively. Regarding the SPEI-6 forecasting results, the ELM-WCA achieved the highest RMSE reduction percentage about 63% and 56% at Beypazari and Nallihan stations, respectively.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap

Publiceringsår

2023-03

Språk

Engelska

Publikation/Tidskrift/Serie

Sustainability (Switzerland)

Volym

15

Issue

5

Dokumenttyp

Artikel i tidskrift

Förlag

MDPI AG

Ämne

  • Physical Geography
  • Water Engineering

Nyckelord

  • water resources
  • drought
  • extreme learning machine
  • optimization
  • SPEI
  • water cycle
  • hydroinformatics

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 2071-1050