Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index
1Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Malaysia
Summary
This study enhances drought forecasting using ensemble empirical mode decomposition (EEMD) with the autoregressive integrated moving average (ARIMA) model. The EEMD-ARIMA approach significantly improves prediction accuracy over the traditional ARIMA method for precipitation data.
Area of Science:
- Hydrology and Climate Science
- Time Series Analysis
- Environmental Monitoring
Background:
- Drought prediction is crucial for water resource management and disaster preparedness.
- Traditional forecasting models often struggle with the complex, non-linear nature of climate data.
- Accurate drought forecasting requires advanced analytical techniques to capture underlying patterns.
Purpose of the Study:
- To introduce and evaluate a novel ensemble empirical mode decomposition with autoregressive integrated moving average (EEMD-ARIMA) model for drought prediction.
- To compare the performance of the EEMD-ARIMA model against the conventional autoregressive integrated moving average (ARIMA) model.
- To assess forecasting accuracy across multiple timescales of the standardized precipitation index (SPI).
Main Methods:
- Monthly precipitation data from Herat province, Afghanistan (1970-2019) were analyzed.
- Standardized Precipitation Index (SPI) was calculated for timescales of 3, 6, 9, and 12 months.
- Ensemble Empirical Mode Decomposition (EEMD) was applied to decompose SPI series into intrinsic mode functions (IMFs) and a residual.
- Autoregressive Integrated Moving Average (ARIMA) models were used to forecast each IMF and the residual.
- An ensemble forecast was generated by summing the predictions of individual components.
Main Results:
- The EEMD-ARIMA model demonstrated significantly higher drought forecasting accuracy compared to the traditional ARIMA model.
- Statistical indicators including root-mean-square error, mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared confirmed the superiority of the EEMD-ARIMA approach.
- Improved prediction performance was observed across all evaluated SPI timescales (SPI 3, SPI 6, SPI 9, SPI 12).
Conclusions:
- The integration of EEMD with ARIMA provides a robust and accurate method for drought prediction.
- The EEMD-ARIMA model effectively captures complex data features, leading to enhanced forecasting capabilities.
- This advanced approach offers a valuable tool for improving water resource management and mitigating drought impacts in vulnerable regions.
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