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

Author

Listed:
  • Ali Danandeh Mehr

    (Department of Civil Engineering, Antalya Bilim University, Antalya 07190, Turkey
    Centre of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz 51666, Iran)

  • 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
    Faculty of Civil and Environmental Engineering, Near East University, Lefkoşa 99138, Turkey)

  • 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)

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.

Suggested Citation

  • Ali Danandeh Mehr & Rifat Tur & Mohammed Mustafa Alee & Enes Gul & Vahid Nourani & Shahrokh Shoaei & Babak Mohammadi, 2023. "Optimizing Extreme Learning Machine for Drought Forecasting: Water Cycle vs. Bacterial Foraging," Sustainability, MDPI, vol. 15(5), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:3923-:d:1075831
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    References listed on IDEAS

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    1. Arman Pourraeisi & Mohammad Reza Boorboori & Mozhgan Sepehri, 2022. "A Comparison of the Effects of Rhizophagus Intraradices, Serendipita Indica, and Pseudomonas Fluorescens on Soil and Zea Maize L. Properties under Drought Stress Condition," International Journal of Sustainable Agricultural Research, Conscientia Beam, vol. 9(4), pages 152-167.
    2. Hamid Reza Yavari & Amir Robati, 2021. "Developing Water Cycle Algorithm for Optimal Operation in Multi-reservoirs Hydrologic System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2281-2303, June.
    3. Farshad Ahmadi & Saeid Mehdizadeh & Babak Mohammadi, 2021. "Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4127-4147, September.
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    Cited by:

    1. Enes Gul & Efthymia Staiou & Mir Jafar Sadegh Safari & Babak Vaheddoost, 2023. "Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Türkiye," Sustainability, MDPI, vol. 15(15), pages 1-17, July.
    2. Karpagam Sundararajan & Kathiravan Srinivasan, 2024. "A Synergistic Optimization Algorithm with Attribute and Instance Weighting Approach for Effective Drought Prediction in Tamil Nadu," Sustainability, MDPI, vol. 16(7), pages 1-24, April.

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