Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles

doi:10.1016/j.jhydrol.2019.05.089

CORC > 武汉岩土力学研究所 > 中科院武汉岩土力学所 > 岩土力学所知识全产出 > 期刊论文

	Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles
	Chen, Wei ; Hong, Haoyuan ; Li, Shaojun ; Shahabi, Himan ; Wang, Yi ; Wang, Xiaojing ; Bin Ahmad, Baharin
刊名	JOURNAL OF HYDROLOGY
	2019
卷号	575 期号:-页码:864-873
关键词	Flood susceptibility Machine learning Ensemble framework GIS China
ISSN号	0022-1694
DOI	10.1016/j.jhydrol.2019.05.089
英文摘要	Flooding is a very common natural hazard that causes catastrophic effects worldwide. Recently, ensemble-based techniques have become popular in flood susceptibility modelling due to their greater strength and efficiency in the prediction of flood locations. Thus, the aim of this study was to employ machine learning-based Reduced-error pruning trees (REPTree) with Bagging (Bag-REPTree) and Random subspace (RS-REPTree) ensemble frameworks for spatial prediction of flood susceptibility using a geographic information system (GIS). First, a flood spatial database was constructed with 363 flood locations and thirteen flood influencing factors, namely altitude, slope angle, slope aspect, curvature, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), distance to rivers, normalized difference vegetation index (NDVI), soil, land use, lithology, and rainfall. Subsequently, correlation attribute evaluation (CAE) was used as the factor selection method for optimization of input factors. Finally, the receiver operating characteristic (ROC) curve, standard error (SE), confidence interval (CI) at 95%, and Wilcoxon signed-rank test were used to validate and compare the performance of the models. Results show that the RS-REPTree model has the highest prediction capability for flood susceptibility assessment, with the highest area under (the ROC) curve (AUC) value (0.949, 0.907), the smallest SE (0.011, 0.023), and the narrowest CI (95%) (0.928-0.970, 0.863-0.952) for the training and validation datasets. It was followed by the Bag-REPTree and REPTree models, respectively. The results also proved the superiority of the ensemble method over using these methods individually.
WOS研究方向	Engineering ; Geology ; Water Resources
语种	英语
WOS记录号	WOS:000488143000066
内容类型	期刊论文
源URL	[http://119.78.100.198/handle/2S6PX9GI/14919]
专题	岩土力学所知识全产出_期刊论文
作者单位	1.Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Shaanxi, Peoples R China; 2.Minist Land & Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, Shaanxi, Peoples R China; 3.Shaanxi Prov Key Lab Geol Support Coal Green Expl, Xian 710054, Shaanxi, Peoples R China; 4.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Chen, Wei,Hong, Haoyuan,Li, Shaojun,et al. Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles[J]. JOURNAL OF HYDROLOGY,2019,575(-):864-873.
APA	Chen, Wei.,Hong, Haoyuan.,Li, Shaojun.,Shahabi, Himan.,Wang, Yi.,...&Bin Ahmad, Baharin.(2019).Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles.JOURNAL OF HYDROLOGY,575(-),864-873.
MLA	Chen, Wei,et al."Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles".JOURNAL OF HYDROLOGY 575.-(2019):864-873.