An improved adaptive neuro-fuzzy inference system for hydrological drought prediction in Algeria
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Drought has negative impacts on water resources, food security, soil degradation, desertification and agricultural productivity. The meteorological and hydrological droughts prediction using standardized precipitation/runoff indices (SPI/SRI) is crucial for effective water resource management. In this study, we suggest ANFISWCA, an adaptive neuro-fuzzy inference system (ANFIS) optimized by the water cycle algorithm (WCA), for hydrological drought forecasting in semi-arid regions of Algeria. The new model was used to predict SRI at 3-, 6-, 9-, and 12 -month accumulation periods in the Wadi Mina basin, Algeria. The results of the model were assessed using four criteria; determination coefficient, mean absolute error, variance accounted for, and root mean square error, and compared with those of the standalone ANFIS model. The findings suggested that throughout the testing phase at all the sub-basins, the proposed hybrid model outperformed the conventional model for estimating drought. This study indicated that the WCA algorithm enhanced the ANFIS model's drought forecasting accuracy. The pro-posed model could be employed for forecasting drought at multi-timescales, deciding on remedial strategies for dealing with drought at study stations, and aiding in sustainable water resources management.
Açıklama
Anahtar Kelimeler
Hydrological drought, Hybrid model, ANFIS, Water cycle algorithm, semi -arid regions
Kaynak
Physics and Chemistry of The Earth
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
131
