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Prediction of Hydrological Drought in Semi-arid Regions Using a Novel Hybrid Model

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  • Anas Mahmood Al-Juboori

    (University of Mosul)

Abstract

Hydrological drought is one of the most important natural phenomena affecting the various aspects of life on Earth, especially with the increasing impact of climate change around the world. Drought forecasting is one of the important strategies in preparing treatments to reduce the effects of drought on life, especially in water resource management. The process of forecasting hydrological drought is one of the complex issues in hydrology, especially in the semi-arid regions. In the current paper, a hybrid model was developed to predict hydrological drought, using two machine learning models, namely the K-Nearest Neighbor (KNN) and the K-means clusters. The results of the stream drought index for the (3, 6, 9, 12) months-time scale were used to predict the hydrological drought of the study area. The flow data of the Great Zab River in Iraq was used as the case study. The results showed the efficiency of the proposed hybrid model in predicting hydrological drought compared to using the KNN model. The results showed that the results of the hybrid model are better than the results of the KNN model by 85%. Thus, the proposed hybrid model has overcome the difficulty of predicting hydrological drought in semi-arid regions.

Suggested Citation

  • Anas Mahmood Al-Juboori, 2023. "Prediction of Hydrological Drought in Semi-arid Regions Using a Novel Hybrid Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3657-3669, July.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:9:d:10.1007_s11269-023-03520-1
    DOI: 10.1007/s11269-023-03520-1
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    References listed on IDEAS

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    1. Hossein Tabari & Jaefar Nikbakht & P. Hosseinzadeh Talaee, 2013. "Hydrological Drought Assessment in Northwestern Iran Based on Streamflow Drought Index (SDI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 137-151, January.
    2. Reza Zamani & Hossein Tabari & Patrick Willems, 2015. "Extreme streamflow drought in the Karkheh river basin (Iran): probabilistic and regional analyses," 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. 76(1), pages 327-346, March.
    3. I. Nalbantis & G. Tsakiris, 2009. "Assessment of Hydrological Drought Revisited," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(5), pages 881-897, March.
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    Cited by:

    1. Ibrahim Halil Deger & Musa Esıt & Mehmet Ishak Yuce, 2023. "Univariate and Bivariate Hydrological Drought Frequency Analysis by Copula Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4881-4907, September.

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