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1 Acta Geophysica

1 2022

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Drought forecasting using new advanced ensemble-based models of reduced error pruning tree

Shahdad Mojtaba, Saber Behzad

Acta Geophysica

2022

Vol. 70, no 2

697--712

The present study investigates the prediction accuracy of standalone Reduced Error Pruning Tree model and its integration with Bagging (BA), Dagging (DA), Additive Regression (AR) and Random Committee (RC) for drought forecasting on time scales of 3, 6, 12, 48 months ahead using Standard Precipitation Index (SPI), which is among the most common criteria for testing drought prediction, at Kermanshah synoptic station in western Iran. To this end, monthly data obtained from a 31-year period record including rainfall, maximum and minimum temperatures, and maximum and minimum relative humidtty rates were considered as the required input to predict SPI. In addition, different inputs were combined and constructed to determine the most effective parameter. Finally, the obtained results were validated using visual and quantitative criteria. According to the results, the best input combination comprised both meteorological variable and SPI along with lag time. Although hybrid models enhanced the results of standalone models, the accuracy of the best performing models could vary on different SPI time scales. Overall, BA, DA and RC models were much more effective than AR models. Moreover, RMSE value increased from SPI (3) to SPI (48), indicating that performance modeling would become much more challenging and complex on higher time scales. Finally, the performance of the newly developed models was compared with that of conventional and most commonly used Support Vector Machine and Adaptive Neuro-Fuzzy Inference System (ANFIS) models, regarded as the benchmark. The results revealed that all the newly developed models were characterized by higher prediction power than ANFIS and ANN.