The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Babak Mohammadi

Doctoral student

Default user image.

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

Author

  • 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.

Department/s

  • Dept of Physical Geography and Ecosystem Science

Publishing year

2023-03

Language

English

Publication/Series

Sustainability (Switzerland)

Volume

15

Issue

5

Document type

Journal article

Publisher

MDPI AG

Topic

  • Physical Geography
  • Water Engineering

Keywords

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

Status

Published

ISBN/ISSN/Other

  • ISSN: 2071-1050