Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

Achite, Mohammed1,2; Katipoglu, Okan Mert3; Jehanzaib, Muhammad4,5; Elshaboury, Nehal6; Kartal, Veysi7; Ali, Shoaib8

2023-09-01

10.3390/atmos14091447

ATMOSPHERE   Impact Factor & Quartile

2073-4433

Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM's fast-learning ability and discrete wavelet transform's ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria.

standardized runoff index drought indices extreme learning machine wavelet transform Algeria

SCI

English

Others

Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences

Environmental Sciences ; Meteorology & Atmospheric Sciences

WOS:001076952100001

MDPI

Web of Science

1.Hassiba Benbouali Univ Chlef, Fac Nat & Life Sci, Lab Water & Environm, Chlef 02180, Algeria
2.Univ Oran 2 Mohamed Ben Ahmed, Algeria Georessources Environm & Nat Risks Lab, Oran 31000, Algeria
3.Erzincan Binali Yildirim Univ, Dept Civil Engn, TR-24002 Erzincan, Turkiye
4.Hanyang Univ, Res Inst Engn & Technol, Ansan 15588, South Korea
5.Qurtuba Univ Sci & Informat Technol, Dept Civil Engn & Technol, Dera Ismail Khan 29050, Pakistan
6.Construction & Project Management Res Inst, Housing & Bldg Natl Res Ctr, Giza 12311, Egypt
7.Siirt Univ, Engn Fac, Dept Civil Engn, TR-23119 Siirt, Turkiye
8.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen 518055, Peoples R China

Achite, Mohammed,Katipoglu, Okan Mert,Jehanzaib, Muhammad,et al. Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria[J]. ATMOSPHERE,2023,14(9).

Achite, Mohammed,Katipoglu, Okan Mert,Jehanzaib, Muhammad,Elshaboury, Nehal,Kartal, Veysi,&Ali, Shoaib.(2023).Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria.ATMOSPHERE,14(9).

Achite, Mohammed,et al."Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria".ATMOSPHERE 14.9(2023).