干旱区生态环境知识资源中心(Arid): Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting

Arid

DOI	10.3390/w15193413
	Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting
	Ekmekcioglu, Oemer
通讯作者	Ekmekcioglu, O
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:19
英文摘要	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.
英文关键词	drought forecasting hydrology machine learning Mann-Whitney U test sc-PDSI semi-arid climate signal processing wavelet transform variational mode decomposition
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001084423200001
WOS关键词	NEURAL-NETWORK ; WAVELET ; INDEX
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/399080
推荐引用方式 GB/T 7714	Ekmekcioglu, Oemer. Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting[J],2023,15(19).
APA	Ekmekcioglu, Oemer.(2023).Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting.WATER,15(19).
MLA	Ekmekcioglu, Oemer."Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting".WATER 15.19(2023).

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