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An Approach for Future Droughts in Northwest Türkiye: SPI and LSTM Methods

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  • Emine Dilek Taylan

    (Department of Civil Engineering, Engineering and Natural Sciences Faculty, Süleyman Demirel University, Isparta 32260, Türkiye)

Abstract

Predetermining the risk of possible future droughts enables proactive measures to be taken in key areas such as agriculture, water management, and food security. Through these predictions, governments, non-governmental organizations, and farmers can develop water-saving strategies, encourage more efficient use of water, and minimize economic losses that may occur due to drought. Thus, future drought forecasts stand out as a strategic planning tool for the protection of natural resources. To achieve this aim, forecasted drought conditions for the next decade (2024–2034) at nine meteorological stations in the Sakarya basin, located northwest of Türkiye, are examined, using historical monthly precipitation data from 1991 to 2023. This study uses the Standardized Precipitation Index (SPI) and Long Short-Term Memory (LSTM) deep learning methods to investigate future meteorological droughts. The research confirms the compatibility and reliability of the LSTM method for forecasting meteorological droughts by comparing historical and forecasted SPI values’ correlograms and trends. In addition, drought maps are created to visually represent the spatial distribution of the most severe droughts expected in the coming years, and areas at risk of drought in the Sakarya Basin are determined. The study contributes to the limited literature on forward-looking drought forecasts in the Sakarya Basin and provides valuable information for long-term water resource planning and drought management in the region.

Suggested Citation

  • Emine Dilek Taylan, 2024. "An Approach for Future Droughts in Northwest Türkiye: SPI and LSTM Methods," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6905-:d:1454514
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    References listed on IDEAS

    as
    1. Duong Tran Anh & Dat Vi Thanh & Hoang Minh Le & Bang Tran Sy & Ahad Hasan Tanim & Quoc Bao Pham & Thanh Duc Dang & Son T. Mai & Nguyen Mai Dang, 2023. "Effect of Gradient Descent Optimizers and Dropout Technique on Deep Learning LSTM Performance in Rainfall-runoff Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 639-657, January.
    2. Tugrul Varol & Ayhan Atesoglu & Halil Baris Ozel & Mehmet Cetin, 2023. "Copula-based multivariate standardized drought index (MSDI) and length, severity, and frequency of hydrological drought in the Upper Sakarya Basin, Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3669-3683, April.
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