An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index
Article
Article Title | An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index |
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ERA Journal ID | 1956 |
Article Category | Article |
Authors | Ali, Mumtaz (Author), Deo, Ravinesh C. (Author), Downs, Nathan J. (Author) and Maraseni, Tek (Author) |
Journal Title | Atmospheric Research |
Journal Citation | 207, pp. 155-180 |
Number of Pages | 26 |
Year | 2018 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0169-8095 |
1873-2895 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.atmosres.2018.02.024 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0169809517311596 |
Abstract | 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. |
Keywords | standardized precipitation index; drought forecasting; ensemble based adaptive neuro fuzzy inference system; M5 tree; minimax probability machine regression |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
370105. Atmospheric dynamics | |
469999. Other information and computing sciences not elsewhere classified | |
370108. Meteorology | |
460207. Modelling and simulation | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Institute for Agriculture and the Environment | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q49vz/an-ensemble-anfis-based-uncertainty-assessment-model-for-forecasting-multi-scalar-standardized-precipitation-index
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