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

Ali Danandeh Mehr, Rifat Tur (), Mohammed Mustafa Alee, Enes Gul, Vahid Nourani, Shahrokh Shoaei and Babak Mohammadi
Additional contact information
Ali Danandeh Mehr: Department of Civil Engineering, Antalya Bilim University, Antalya 07190, Turkey
Rifat Tur: Department of Civil Engineering, Akdeniz University, Antalya 07070, Turkey
Mohammed Mustafa Alee: Department of Information Technology, Choman Technical Institute, Erbil Polytechnic University, Erbil 44001, Iraq
Enes Gul: Department of Civil Engineering, Inonu University, Inonu 44280, Turkey
Vahid Nourani: Centre of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz 51666, Iran
Shahrokh Shoaei: Department of Civil Engineering, Payame Noor University, Tabriz Branch, Tabriz 51748, Iran
Babak Mohammadi: Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden

Sustainability, 2023, vol. 15, issue 5, 1-17

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 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.

Keywords: drought; SPEI; extreme learning machine; water cycle; bacterial forging; optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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