1. Hybrid models for drought forecasting: Integration of multi pre-processing-data driven approaches and non-linear GARCH time series model.
- Author
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Ghasempour, Roghayeh, Roushangar, Kiyoumars, and Alizadeh, Farhad
- Subjects
DROUGHT forecasting ,TIME series analysis ,STANDARD deviations ,DISCRETE wavelet transforms ,TREE pruning ,RANDOM forest algorithms - Abstract
This study introduces two integrated methods to model the short- to long-term droughts in terms of the Standardized Precipitation Index (SPI). In this regard, the monthly precipitation data from three stations located in different climates of Iran was used. Firstly, the capability of Random Forest (RF) and M5 Pruning tree (M5P) models were assessed for short- to long-term drought forecasting. Then, the impact of data multi-pre-processing on improving the models' efficiency was investigated. Therefore, inputs were serially decomposed via Discrete Wavelet Transform (DWT) and Variational Mode Decomposition (VMD). The most dominate subseries were selected based on their energy values and used as inputs of the RF and M5P. Furthermore, since the drought variable consists of two deterministic and stochastic sections and in the modeling process the stochastic part is not often considered, in the third step, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model was applied for determining the random parts of the SPIs. Results showed that the integrated models led to better outcomes compared to the standalone models. The second and third steps increased the modeling accuracy up to 45% and 55%, respectively. Results showed that the distribution range of the Root Mean Square Errors (RMSE) criteria for the SPI-3 modeling decreased from 0.438–0.551 (in using raw data) to 0.184–0.26 (in using the integrated-GARCH model). Results showed that using the integrated-GARCH models, SPIs could be successfully simulated with considering only one input variable. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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