Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting
The current study seeks to conduct time series forecasting of droughts by means of the state-of-the-art XGBoost algorithm. To explore the drought variability in one of the semi-arid regions of Turkey, i.e., Denizli, the self-calibrated Palmer Drought Severity Index (sc-PDSI) values were used and pro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/19/3413 |
| _version_ | 1797447179731730432 |
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| author | Ömer Ekmekcioğlu |
| author_facet | Ömer Ekmekcioğlu |
| author_sort | Ömer Ekmekcioğlu |
| collection | DOAJ |
| description | The current study seeks to conduct time series forecasting of droughts by means of the state-of-the-art XGBoost algorithm. To explore the drought variability in one of the semi-arid regions of Turkey, i.e., Denizli, the self-calibrated Palmer Drought Severity Index (sc-PDSI) values were used and projections were made for different horizons, including short-term (1-month: t + 1), mid-term (3-months: t + 3 and 6-months: t + 6), and long-term (12-months: t + 12) periods. The original sc-PDSI time series was subjected to the partial autocorrelation function to identify the input configurations and, accordingly, one- (t − 1) and two-month (t − 2) lags were used to perform the forecast of the targeted outcomes. This research further incorporated the recently introduced variational mode decomposition (VMD) for signal processing into the predictive model to enhance the accuracy. The proposed model was not only benchmarked with the standalone XGBoost but also with the model generated by its hybridization with the discrete wavelet transform (DWT). The overall results revealed that the VMD-XGBoost model outperformed its counterparts in all lead-time forecasts with NSE values of 0.9778, 0.9405, 0.8476, and 0.6681 for t + 1, t + 3, t + 6, and t + 12, respectively. Transparency of the proposed hybrid model was further ensured by the Mann–Whitney U test, highlighting the results as statistically significant. |
| first_indexed | 2024-03-09T13:51:10Z |
| format | Article |
| id | doaj.art-74f503d0b3d64839aa325f1dbbbe6a8b |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T13:51:10Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-74f503d0b3d64839aa325f1dbbbe6a8b2023-11-30T20:49:46ZengMDPI AGWater2073-44412023-09-011519341310.3390/w15193413Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient BoostingÖmer Ekmekcioğlu0Disaster and Emergency Management Department, Disaster Management Institute, Istanbul Technical University, 34469 Istanbul, TurkeyThe current study seeks to conduct time series forecasting of droughts by means of the state-of-the-art XGBoost algorithm. To explore the drought variability in one of the semi-arid regions of Turkey, i.e., Denizli, the self-calibrated Palmer Drought Severity Index (sc-PDSI) values were used and projections were made for different horizons, including short-term (1-month: t + 1), mid-term (3-months: t + 3 and 6-months: t + 6), and long-term (12-months: t + 12) periods. The original sc-PDSI time series was subjected to the partial autocorrelation function to identify the input configurations and, accordingly, one- (t − 1) and two-month (t − 2) lags were used to perform the forecast of the targeted outcomes. This research further incorporated the recently introduced variational mode decomposition (VMD) for signal processing into the predictive model to enhance the accuracy. The proposed model was not only benchmarked with the standalone XGBoost but also with the model generated by its hybridization with the discrete wavelet transform (DWT). The overall results revealed that the VMD-XGBoost model outperformed its counterparts in all lead-time forecasts with NSE values of 0.9778, 0.9405, 0.8476, and 0.6681 for t + 1, t + 3, t + 6, and t + 12, respectively. Transparency of the proposed hybrid model was further ensured by the Mann–Whitney U test, highlighting the results as statistically significant.https://www.mdpi.com/2073-4441/15/19/3413drought forecastinghydrologymachine learningMann–Whitney U testsc-PDSIsemi-arid climate |
| spellingShingle | Ömer Ekmekcioğlu Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting Water drought forecasting hydrology machine learning Mann–Whitney U test sc-PDSI semi-arid climate |
| title | Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting |
| title_full | Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting |
| title_fullStr | Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting |
| title_full_unstemmed | Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting |
| title_short | Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting |
| title_sort | drought forecasting using integrated variational mode decomposition and extreme gradient boosting |
| topic | drought forecasting hydrology machine learning Mann–Whitney U test sc-PDSI semi-arid climate |
| url | https://www.mdpi.com/2073-4441/15/19/3413 |
| work_keys_str_mv | AT omerekmekcioglu droughtforecastingusingintegratedvariationalmodedecompositionandextremegradientboosting |