DOI: 10.1007/s11069-021-04550-x
论文题名: Multi-timescale drought prediction using new hybrid artificial neural network models
作者: Banadkooki F.B. ; Singh V.P. ; Ehteram M.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 106, 期: 3 起始页码: 2461
结束页码: 2478
语种: 英语
中文关键词: Drought estimation
; Groundwater index
; Salp swarm algorithm
; Soft computing models
英文关键词: algorithm
; artificial neural network
; computer simulation
; drought
; error analysis
; estimation method
; groundwater resource
; numerical model
; prediction
; resource assessment
; resource management
英文摘要: In this study, new hybrid artificial neural network (ANN) models were used for predicting the groundwater resource index. The salp swarm algorithm (SSA), particle swarm optimization (PSO), and genetic algorithm (GA) were used to find the weight and bias values of the ANN models. The ANN-PSO, ANN-SSA and ANN-GA models were used to predict the groundwater resource index (GRI)-based drought at different timescales (6, 12, and 24 months) in Yazd plain, Iran. Five input scenarios were used for modeling GRI. The best input scenario was a combination of one-month-lagged GRI, two-month-lagged GRI, three-month-lagged GRI, four-month-lagged GRI, and five-month-lagged GRI, which is known as the fifth input scenario. The outputs of models indicated that the ANN-SSA model with input scenario (5) decreased the mean absolute error (MAE) of ANN-PSO (5) and ANN-GA (5) by 43% and 51%, respectively. Among the hybrid ANN models, ANN-SSA (5), ANN-PSO (5) and ANN-GA (5) outperformed the other hybrid ANN models. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168898
Appears in Collections: 气候变化与战略
There are no files associated with this item.
作者单位: Agricultural Department, Payame Noor University, Tehran, Iran; Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843-2117, United States; Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
Recommended Citation:
Banadkooki F.B.,Singh V.P.,Ehteram M.. Multi-timescale drought prediction using new hybrid artificial neural network models[J]. Natural Hazards,2021-01-01,106(3)