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Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Türkiye

Author

Listed:
  • Enes Gul

    (Department of Civil Engineering, Inonu University, Malatya 44000, Türkiye)

  • Efthymia Staiou

    (Department of Industrial Engineering, Yasar University, Izmir 35100, Türkiye)

  • Mir Jafar Sadegh Safari

    (Department of Civil Engineering, Yasar University, Izmir 35100, Türkiye)

  • Babak Vaheddoost

    (Department of Civil Engineering, Bursa Technical University, Bursa 16310, Türkiye)

Abstract

The impact of climate change has led to significant changes in hydroclimatic patterns and continuous stress on water resources through frequent wet and dry spells. Hence, understanding and effectively addressing the escalating impact of climate change on hydroclimatic patterns, especially in the context of meteorological drought, necessitates precise modeling of these phenomena. This study focuses on assessing the accuracy of drought modeling using the well-established Standard Precipitation Index (SPI) in the Aegean region of Türkiye. The study utilizes monthly precipitation data from six stations in Cesme, Kusadasi, Manisa, Seferihisar, Selcuk and Izmir at Kucuk Menderes Basin covering the period from 1973 to 2020. The dataset is divided into three sets, training (60%), validation (20%), and testing (20%) sets. The study aims to determine the SPI-3, SPI-6 and SPI-12 using a multi-station prediction technique. Three boosting regression models (BRMs), namely Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting (GradBoost), were employed and optimized with the help of the Weighted Mean of Vectors (INFO) technique. Model performances were then evaluated with the Root Mean Square Error ( RMSE ), Mean Absolute Error ( MAE ), Mean Absolute Percentage Error ( MAPE ), Coefficient of Determination ( R 2 ) and the Willmott Index ( WI ). Results demonstrated a distinct superiority of the XgBoost model over AdaBoost and GradBoost in terms of accuracy. During the test phase, the XgBoost model achieved RMSEs of 0.496, 0.429 and 0.389 for SPI-3, SPI-6 and SPI-12, respectively. The WIs were 0.899, 0.901 and 0.825 for SPI-3, SPI-6 and SPI-12, respectively. These are considerably lower than the corresponding values obtained by the other models. Yet, the comparative statistical analysis further underscores the effectiveness of XgBoost in modeling extended periods of drought in the Aegean region of Türkiye.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11568-:d:1203203
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    References listed on IDEAS

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    Cited by:

    1. Murat Can, 2024. "Invasive-Weed-Optimization-Based Extreme Learning Machine for Prediction of Lake Water Level Using Major Atmospheric–Oceanic Climate Scenarios," Sustainability, MDPI, vol. 16(17), pages 1-13, September.
    2. Ioannis M. Kourtis & Harris Vangelis & Dimitris Tigkas & Anna Mamara & Ioannis Nalbantis & George Tsakiris & Vassilios A. Tsihrintzis, 2023. "Drought Assessment in Greece Using SPI and ERA5 Climate Reanalysis Data," Sustainability, MDPI, vol. 15(22), pages 1-19, November.

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