An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index
By Mumtaz Ali, Ravinesh C. Deo, N. Downs, Tek Maraseni. Atmospheric research, 2018
75 citations recorded by the source corpus.
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.
Subfields: Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/ed976f346c5e9f29f5806bf94f0ab1c3282af003
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
In Global and Planetary Change
Citations in the corpus
- Meteorological drought prediction using heuristic approaches based on effective drought index: a case study in Uttarakhand (2020, methodology): “…studies conducted on meteorological drought forecasting using machine learning and stochastic models based on various drought indices such as (Mishra and Desai 2006; Belayneh et al. 2014; Deo and Şahin, 2015a, b; Deo et al. 2017a; Mouatadid et al. 2018; Ali et al. 2018; Abbasi et al. 2019).”