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Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles
文献类型：期刊论文
| 作者 | Chen, Wei; Hong, Haoyuan; Li, Shaojun; Shahabi, Himan; Wang, Yi; Wang, Xiaojing; Bin Ahmad, Baharin |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 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 |
| 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 国家重点实验室知识产出_期刊论文 |
| 作者单位 | 1.Shaanxi Prov Key Lab Geol Support Coal Green Expl, Xian 710054, Shaanxi, Peoples R China; 2.Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Shaanxi, Peoples R China; 3.Minist Land & Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, 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. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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