DOI: 10.1016/j.atmosres.2018.02.024
Scopus记录号: 2-s2.0-85044454907
论文题名: An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index
作者: Ali M. ; Deo R.C. ; Downs N.J. ; Maraseni T.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2018
卷: 207 起始页码: 155
结束页码: 180
语种: 英语
英文关键词: Drought forecasting
; Ensemble based adaptive neuro fuzzy inference system
; M5 tree
; Minimax probability machine regression
; Standardized precipitation index
Scopus关键词: Drought
; Errors
; Forestry
; Fuzzy neural networks
; Fuzzy systems
; Mean square error
; Risk perception
; Stream flow
; Uncertainty analysis
; Weather forecasting
; Adaptive neuro-fuzzy inference system
; Drought forecasting
; M5 tree
; Minimax probability machine
; Standardized precipitation index
; Fuzzy inference
; artificial neural network
; drought
; ensemble forecasting
; modeling
; precipitation assessment
; uncertainty analysis
; Pakistan
英文摘要: Forecasting drought by means of the World Meteorological Organization-approved Standardized Precipitation Index (SPI) is considered to be a fundamental task to support socio-economic initiatives and effectively mitigating the climate-risk. This study aims to develop a robust drought modelling strategy to forecast multi-scalar SPI in drought-rich regions of Pakistan where statistically significant lagged combinations of antecedent SPI are used to forecast future SPI. With ensemble-Adaptive Neuro Fuzzy Inference System (‘ensemble-ANFIS’) executed via a 10-fold cross-validation procedure, a model is constructed by randomly partitioned input-target data. Resulting in 10-member ensemble-ANFIS outputs, judged by mean square error and correlation coefficient in the training period, the optimal forecasts are attained by the averaged simulations, and the model is benchmarked with M5 Model Tree and Minimax Probability Machine Regression (MPMR). The results show the proposed ensemble-ANFIS model's preciseness was notably better (in terms of the root mean square and mean absolute error including the Willmott's, Nash-Sutcliffe and Legates McCabe's index) for the 6- and 12- month compared to the 3-month forecasts as verified by the largest error proportions that registered in smallest error band. Applying 10-member simulations, ensemble-ANFIS model was validated for its ability to forecast severity (S), duration (D) and intensity (I) of drought (including the error bound). This enabled uncertainty between multi-models to be rationalized more efficiently, leading to a reduction in forecast error caused by stochasticity in drought behaviours. Through cross-validations at diverse sites, a geographic signature in modelled uncertainties was also calculated. Considering the superiority of ensemble-ANFIS approach and its ability to generate uncertainty-based information, the study advocates the versatility of a multi-model approach for drought-risk forecasting and its prime importance for estimating drought properties over confidence intervals to generate better information for strategic decision-making. © 2018 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/108895
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: School of Agricultural, Computational and Environmental Sciences, Institute of Agriculture and Environment, University of Southern Queensland, Springfield, QLD 4300, Australia
Recommended Citation:
Ali M.,Deo R.C.,Downs N.J.,et al. An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index[J]. Atmospheric Research,2018-01-01,207