Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index
This study introduces ensemble empirical mode decomposition (EEMD) coupled with the autoregressive integrated moving average (ARIMA) model for drought prediction. In the realm of drought forecasting, we assess the EEMD-ARIMA model against the traditional ARIMA approach, using monthly precipitation d...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2024-02-01
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| Series: | Water Science and Technology |
| Subjects: | |
| Online Access: | http://wst.iwaponline.com/content/89/3/745 |
| _version_ | 1827349134570422272 |
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| author | Reza Rezaiy Ani Shabri |
| author_facet | Reza Rezaiy Ani Shabri |
| author_sort | Reza Rezaiy |
| collection | DOAJ |
| description | This study introduces ensemble empirical mode decomposition (EEMD) coupled with the autoregressive integrated moving average (ARIMA) model for drought prediction. In the realm of drought forecasting, we assess the EEMD-ARIMA model against the traditional ARIMA approach, using monthly precipitation data from January 1970 to December 2019 in Herat province, Afghanistan. Our evaluation spans various timescales of standardized precipitation index (SPI) 3, SPI 6, SPI 9, and SPI 12. Statistical indicators like root-mean-square error, mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 are employed. To comprehend data features thoroughly, each SPI series initially computed from the original monthly precipitation time series. Subsequently, each SPI undergoes decomposition using EEMD, resulting in intrinsic mode functions (IMFs) and one residual series. The next step involves forecasting each IMF component and residual using the corresponding ARIMA model. To create an ensemble forecast for the initial SPI series, the predicted outcomes of the modeled IMFs and residual series are finally added. Results indicate that EEMD-ARIMA significantly enhances drought forecasting accuracy compared to conventional ARIMA model.
HIGHLIGHTS
Improved drought forecasting: Our study introduces the ensemble empirical mode decomposition–autoregressive integrated moving average (ARIMA) model, enhancing drought forecasting accuracy over traditional ARIMA methods.;
New drought model for Western Afghanistan: We present a customized model for drought prediction in Western Afghanistan, based on the standardized precipitation index.; |
| first_indexed | 2024-03-08T00:25:40Z |
| format | Article |
| id | doaj.art-5576730144704e119818b249b77816fa |
| institution | Directory Open Access Journal |
| issn | 0273-1223 1996-9732 |
| language | English |
| last_indexed | 2024-03-08T00:25:40Z |
| publishDate | 2024-02-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Science and Technology |
| spelling | doaj.art-5576730144704e119818b249b77816fa2024-02-15T16:19:56ZengIWA PublishingWater Science and Technology0273-12231996-97322024-02-0189374577010.2166/wst.2024.028028Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation indexReza Rezaiy0Ani Shabri1 Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Malaysia Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Malaysia This study introduces ensemble empirical mode decomposition (EEMD) coupled with the autoregressive integrated moving average (ARIMA) model for drought prediction. In the realm of drought forecasting, we assess the EEMD-ARIMA model against the traditional ARIMA approach, using monthly precipitation data from January 1970 to December 2019 in Herat province, Afghanistan. Our evaluation spans various timescales of standardized precipitation index (SPI) 3, SPI 6, SPI 9, and SPI 12. Statistical indicators like root-mean-square error, mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 are employed. To comprehend data features thoroughly, each SPI series initially computed from the original monthly precipitation time series. Subsequently, each SPI undergoes decomposition using EEMD, resulting in intrinsic mode functions (IMFs) and one residual series. The next step involves forecasting each IMF component and residual using the corresponding ARIMA model. To create an ensemble forecast for the initial SPI series, the predicted outcomes of the modeled IMFs and residual series are finally added. Results indicate that EEMD-ARIMA significantly enhances drought forecasting accuracy compared to conventional ARIMA model. HIGHLIGHTS Improved drought forecasting: Our study introduces the ensemble empirical mode decomposition–autoregressive integrated moving average (ARIMA) model, enhancing drought forecasting accuracy over traditional ARIMA methods.; New drought model for Western Afghanistan: We present a customized model for drought prediction in Western Afghanistan, based on the standardized precipitation index.;http://wst.iwaponline.com/content/89/3/745arimadrought forecastingeemdeemd-arimasarimaspi |
| spellingShingle | Reza Rezaiy Ani Shabri Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index Water Science and Technology arima drought forecasting eemd eemd-arima sarima spi |
| title | Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index |
| title_full | Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index |
| title_fullStr | Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index |
| title_full_unstemmed | Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index |
| title_short | Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index |
| title_sort | enhancing drought prediction precision with eemd arima modeling based on standardized precipitation index |
| topic | arima drought forecasting eemd eemd-arima sarima spi |
| url | http://wst.iwaponline.com/content/89/3/745 |
| work_keys_str_mv | AT rezarezaiy enhancingdroughtpredictionprecisionwitheemdarimamodelingbasedonstandardizedprecipitationindex AT anishabri enhancingdroughtpredictionprecisionwitheemdarimamodelingbasedonstandardizedprecipitationindex |