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.
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References
Alawsi MA, Zubaidi SL, Al-Bdairi NSS, Al-Ansari N, Hashim K (2022) Drought forecasting: a Review and Assessment of the hybrid techniques and DataPre-Processing. Hydrology 115:1–23. https://doi.org/10.3390/hydrology9070115
Başakın EE, Ekmekcioğlu Ö, Özger M (2021) Drought prediction using hybrid soft-computing methods for semi-arid region. Model Earth Syst Environ 7:2363–2371. https://doi.org/10.1007/s40808-020-01010-6
Belayneh A, Adamowski J, Khalil B (2016) Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods. Sustain Water Resour Manage 2:87–101. https://doi.org/10.1007/s40899-015-0040-5
Borji M et al (2016) Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN). Arab J Geosci 9:725. https://doi.org/10.1007/s12517-016-2750-x
Cover TM, Hart, Peter E (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27. https://doi.org/10.1109/TIT.1967.1053964
Ding Y, Yu G, Tian R, Sun Y (2022) Application of a hybrid CEEMD-LSTM model based on the standardized precipitation index for Drought forecasting: the case of the Xinjiang Uygur Autonomous Region, China. Atmosphere 13:1504. https://doi.org/10.3390/atmos13091504
Fix E, Hodges JL (1951) Discriminatory analysis. Nonparametric discrimination: consistency Properties (Report). USAF School of Aviation Medicine. Randolph Field, Texas
Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. J royal Stat Soc Ser c (applied statistics) 28:100–108
Hong X, Guo S, Zhou Y et al (2015) Uncertainties in assessing hydrological drought using streamflow drought index for the upper Yangtze River basin. Stoch Environ Res Risk Assess 29:1235–1247. https://doi.org/10.1007/s00477-014-0949-5
Hosseini-Moghari S, Araghinejad S, Azarnivand A (2017) Drought forecasting using data-driven methods and an evolutionary algorithm. Model Earth Syst Environ 3:1675–1689. https://doi.org/10.1007/s40808-017-0385-x
Khan MMH, Muhammad NS, El-Shafie A (2020) Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting. J Hydrol 590:125380. https://doi.org/10.1016/j.jhydrol.2020.125380
Kisi O, Docheshmeh GA, Zounemat-Kermani M, Mahdavi-Meymand A, Kim S (2019) Drought forecasting using novel heuristic methods in a semi-arid environment. J Hydrol 578:124053. https://doi.org/10.1016/j.jhydrol.2019.124053
Liu Y, Wang LH, Yang LB, Liu XM (2022) Drought prediction based on an improved VMD-OS-QR-ELM model. PLoS ONE 17:0262329. https://doi.org/10.1371/journal.pone.0262329
Luhaim ZB, Tan ML, Tangang F, Zulkafli Z, Chun KP, Yusop Z, Yaseen ZM (2021) Drought variability and characteristics in the Muda River Basin of Malaysia from 1985 to 2019. Atmosphere 12:1210. https://doi.org/10.3390/atmos12091210
McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society, Boston, MA, 179–184
Modaresi Rad A, Khalili D, Kamgar-Haghighi AA et al (2016) Assessment of seasonal characteristics of streamflow droughts under semiarid conditions. Nat Hazards 82:1541–1564. https://doi.org/10.1007/s11069-016-2256-6
Mokhtarzad M, Eskandari F, Arabasadi VNJ, A (2017) Drought forecasting by ANN, ANFIS, and SVM and comparison of the models. Environ Earth Sci 76:729. https://doi.org/10.1007/s12665-017-7064-0
Nalbantis I, Tsakiris G (2009) Assessment of Hydrological Drought Revisited. Water Resour Manage 23:881–897 (2009). https://doi.org/10.1007/s11269-008-9305-1
Pande CB, Kushwaha NL, Orimoloye IR et al (2023) Comparative Assessment of Improved SVM Method under different Kernel Functions for Predicting Multi-scale Drought Index. Water Resour Manage 37:1367–1399. https://doi.org/10.1007/s11269-023-03440-0
Rad AM, Khalili D, Kamgar-Haghighi AA et al (2016) Assessment of seasonal characteristics of streamflow droughts under semiarid conditions. Nat Hazards 82:1541–1564. https://doi.org/10.1007/s11069-016-2256-6
Rezaeianzadeh M, Stein A, Cox JP (2016) Drought forecasting using Markov Chain Model and Artificial neural networks. Water Resour Manage 30:2245–2259. https://doi.org/10.1007/s11269-016-1283-0
Roushangara K, Ghasempoura R, Kircac VSO, Demirel MC (2021) Hybrid point and interval prediction approaches for drought modeling using ground-based and remote sensing data. Hydrol Res 52:1469. https://doi.org/10.2166/nh.2021.028
Tabari H, Nikbakht J, Hosseinzadeh Talaee P (2013) Hydrological Drought Assessment in Northwestern Iran based on Streamflow Drought Index (SDI). Water Resour Manage 27:137–151. https://doi.org/10.1007/s11269-012-0173-3
Tabari H, Zamani R, Rahmati H et al (2016) Markov Chains of different orders for Streamflow Drought Analysis. Water Resour Manage 29:3441–3457. https://doi.org/10.1007/s11269-015-1010-2
Tigkas D, Vangelis H, Tsakiris G (2012) Drought and climatic change impact on streamflow in small watersheds. Sci Total Environ 440:33–41. https://doi.org/10.1016/j.scitotenv.2012.08.035
Zamani R, Tabari H, Willems P (2015) Extreme streamflow drought in the Karkheh River basin (Iran): probabilistic and regional analyses. Nat Hazards 76:327–346. https://doi.org/10.1007/s11069-014-1492-x
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Al-Juboori, A.M. Prediction of Hydrological Drought in Semi-arid Regions Using a Novel Hybrid Model. Water Resour Manage 37, 3657–3669 (2023). https://doi.org/10.1007/s11269-023-03520-1
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DOI: https://doi.org/10.1007/s11269-023-03520-1