Optimizing Extreme Learning Machine for Drought Forecasting : Water Cycle vs. Bacterial Foraging
Danandeh Mehr, Ali ; Tur, Rifat ; Alee, Mohammed Mustafa ; Gul, Enes ; Nourani, Vahid ; Shoaei, Shahrokh and Mohammadi, Babak LU
(2023)
In Sustainability (Switzerland)
15(5).
- Abstract
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... (More)
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.
(Less)
- author
- Danandeh Mehr, Ali
; Tur, Rifat
; Alee, Mohammed Mustafa
; Gul, Enes
; Nourani, Vahid
; Shoaei, Shahrokh
and Mohammadi, Babak
LU
- organization
- publishing date
- 2023-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- water resources, drought, extreme learning machine, optimization, SPEI, water cycle, hydroinformatics
- in
- Sustainability (Switzerland)
- volume
- 15
- issue
- 5
- article number
- 3923
- publisher
- MDPI AG
- external identifiers
-
- scopus:85151128508
- ISSN
- 2071-1050
- DOI
- 10.3390/su15053923
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2023 by the authors.
- id
- cdb7c29a-aac8-4809-8ea3-6ccb58249c04
- date added to LUP
- 2023-05-02 16:15:25
- date last changed
- 2024-01-24 11:00:18
@article{cdb7c29a-aac8-4809-8ea3-6ccb58249c04,
abstract = {{<p>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.</p>}},
author = {{Danandeh Mehr, Ali and Tur, Rifat and Alee, Mohammed Mustafa and Gul, Enes and Nourani, Vahid and Shoaei, Shahrokh and Mohammadi, Babak}},
issn = {{2071-1050}},
keywords = {{water resources; drought; extreme learning machine; optimization; SPEI; water cycle; hydroinformatics}},
language = {{eng}},
number = {{5}},
publisher = {{MDPI AG}},
series = {{Sustainability (Switzerland)}},
title = {{Optimizing Extreme Learning Machine for Drought Forecasting : Water Cycle vs. Bacterial Foraging}},
url = {{http://dx.doi.org/10.3390/su15053923}},
doi = {{10.3390/su15053923}},
volume = {{15}},
year = {{2023}},
}