气候变化领域集成服务门户(CCPortal): Multiple spatio-temporal scale runoff forecasting and driving mechanism exploration by K-means optimized XGBoost and SHAP

CCPortal

DOI	10.1016/j.jhydrol.2024.130650
	Multiple spatio-temporal scale runoff forecasting and driving mechanism exploration by K-means optimized XGBoost and SHAP
发表日期	2024
ISSN	0022-1694
EISSN	1879-2707
起始页码	630
卷号	630
英文摘要	Hydrological simulations have seen extensive use of machine learning (ML) models. However, the existing ML models face challenges in effectively handling temporal and spatial heterogeneity of the driving features and transparency of features. To improve the runoff prediction ability of ML and the confusion of runoff generation mechanism interpreted by ML, this study combined K-means with XGBoost (Extreme gradient boosting,) and SHAP (Shapely additive explanations,) to develop an interpretable ML-based hydrological model (KXGBoost) across the Continental United States as a case study. The results show that K-means clustering based on the interpretation of SHAP can effectively capture the temporal and spatial heterogeneity of runoff-driven features. And the performance and interpretability of data-driven hydrological models in runoff simulation can be significantly improved by KXGBoost. KXGBoost yields an NSE (Nash-Sutcliffe Efficiency) of 0.803 and 0.596 during the training and testing, respectively, representing an improvement of 0.089 and 0.029 as compared to the XGBoost model. KXGBoost has demonstrated its ability to identify multiple runoff generation mechanisms under multiple spatio-temporal perspectives. This study provided a novel perspective for understanding hydrological processes and improving runoff simulation and demonstrates the potential of ML-based hydrological models in water resources management.
英文关键词	Runoff; Machine learning; XGBoost; K-means; Shapely additive explanations
语种	英语
WOS研究方向	Engineering ; Geology ; Water Resources
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS记录号	WOS:001186811200001
来源期刊	JOURNAL OF HYDROLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/295180
作者单位	Ocean University of China; Ocean University of China; Ocean University of China; Shandong University
推荐引用方式 GB/T 7714	. Multiple spatio-temporal scale runoff forecasting and driving mechanism exploration by K-means optimized XGBoost and SHAP[J],2024,630.
APA	(2024).Multiple spatio-temporal scale runoff forecasting and driving mechanism exploration by K-means optimized XGBoost and SHAP.JOURNAL OF HYDROLOGY,630.
MLA	"Multiple spatio-temporal scale runoff forecasting and driving mechanism exploration by K-means optimized XGBoost and SHAP".JOURNAL OF HYDROLOGY 630(2024).

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