A multi-model data fusion methodology for seasonal drought forecasting under uncertainty: Application of Bayesian maximum entropy

Abstract

In this paper, we present a new methodology for improving the results of seasonal drought forecasting by developing a Bayesian Maximum Entropy-based fusion (BMEF) model. The BMEF model combines the forecasts done by four individual (single-source) data-driven models to achieve better outcomes. Regional drought indices of Effective Drought Index (EDI) and Multiple Standard Precipitation Index (MSPI) are forecasted using the individual forecasting models of Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), and M5tree. The outputs of the individual models with the best performances are selected to be fused using the BMEF model and the results are analyzed and compared. The effect of different large-scale climate signals on rainfall and drought forecasting is analyzed and the most effective climate variables are selected as predictors in the forecasting models. Next, the uncertainty analysis on the results of the individual models as well as those of the BMEF model is carried out by deriving the probability mass functions of the drought indices using a resampling technique and Monte Carlo analysis. Finally, the results of the uncertainty analysis are evaluated to compare the performance of individual models and the BME-based fusion model in decreasing the uncertainty of seasonal drought forecasting. The performance of the proposed methodology is evaluated by using it to forecast seasonal drought conditions in the southwest of Iran. Based on the results of the uncertainty analysis, the BMEF model provides more reliable forecasts particularly for severe drought events than the individual models. It is also inferred that adding the SST to the predictors, decreases the uncertainty of drought forecasts.