Evaluation of Machine Learning Techniques for Hydrological Drought Modeling: A Case Study of the Wadi Ouahrane Basin in Algeria

被引:40
作者
Achite, Mohammed [1 ,2 ]
Jehanzaib, Muhammad [3 ]
Elshaboury, Nehal [4 ]
Kim, Tae-Woong [5 ]
机构
[1] Hassiba Benbouali Univ Chlef, Fac Nat & Life Sci, Lab Water & Environm, Chlef 02180, Algeria
[2] ENSA, Natl Higher Sch Agron, Algiers 16200, Algeria
[3] Hanyang Univ, Res Inst Engn & Technol, Ansan 15588, South Korea
[4] Housing & Bldg Natl Res Ctr, Construct & Project Management Res Inst, Giza 12311, Egypt
[5] Hanyang Univ, Dept Civil & Environm Engn, Ansan 15588, South Korea
关键词
drought modeling; machine learning; support vector machine; Algeria; ARTIFICIAL-INTELLIGENCE MODELS; SOLAR-RADIATION; RIVER-BASIN; PREDICTION; INDEX; WAVELET; FLOOD; SPEI; SPI;
D O I
10.3390/w14030431
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Forecasting meteorological and hydrological drought using standardized metrics of rainfall and runoff (SPI/SRI) is critical for the long-term planning and management of water resources at the global and regional levels. In this study, various machine learning (ML) techniques including four methods (i.e., ANN, ANFIS, SVM, and DT) were utilized to construct hydrological drought forecasting models in the Wadi Ouahrane basin in the northern part of Algeria. The performance of ML models was assessed using evaluation criteria, including RMSE, MAE, NSE, and R-2. The results showed that all the ML models accurately predicted hydrological drought, while the SVM model outperformed the other ML models, with the average RMSE = 0.28, MAE = 0.19, NSE = 0.86, and R-2 = 0.90. The coefficient of determination of SVM was 0.95 for predicting SRI at the 12-months timescale; as the timescale moves from higher to lower (12 months to 3 months), R-2 starts decreasing.
引用
收藏
页数:18
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