Development of a region-partitioning method for debris flow susceptibility mapping

Qiao, Shuangshuang; Qin, Shengwu; Sun, Jingbo; Che, Wenchao; Yao, Jingyu; Su, Gang; Chen, Yang; Nnanwuba, Uzodigwe Emmanuel

doi:10.1007/s11629-020-6497-1

Cited by 10 publications

(1 citation statement)

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“…Therein, the watershed unit can better reflect the geometric attributes of debris-flow, while the area coverage of a single watershed is large, so the bias may be caused in the allocation of hazard factors in each watershed. Also, this may generate the susceptibility drawing results with only one or two units at a certain susceptibility level, which is unreasonable (Qiao et al 2021). The slope unit above can better reflect the natural conditions of debris-flow development and has a small area, but numerous small or unreasonable polygons may be generated in the flat area after completing the slope unit division, thereby leading to errors in the susceptibility modeling process (Huang et al 2020b).…”

Section: Debris-flow Susceptibility Assessment Unitmentioning

confidence: 99%

Debris-Flow Susceptibility assessment Using Stacking Ensemble Learning Including Multiple Heterogeneous Learners with RFE for factor optimization

Zhao

Lin

2023

Preprint

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An accurate assessment of debris-flow susceptibility is of great importance to the prevention and control of debris-flow disasters in mountainous areas. In this study, by applying the recursive feature elimination-random forest (RFE-RF) and the stacking ensemble learning including multiple heterogeneous learners, debris-flow the high accuracy of the debris-flow susceptibility is assessed. As indicated by the results, the very low and low susceptibility zones of debris-flow are mainly concentrated in the eastern and western parts in the study area. The very high and high susceptibility zones are mainly distributed on the two banks of Xiaojiang River Valley and the south bank of Jinsha River, where there is fragile geological environment and high risk, in the study area. The medium susceptibility zone is mainly distributed around the very high and high susceptibility zones. There are excellent accuracy and stability in the stacking ensemble learning model of debris-flow susceptibility in the mountainous areas, when combining with the RFE-RF model and the diversity measurement. As for the stacking ensemble learning therein, the area under curve (AUC) value of the receiver-operating characteristic (ROC), the accuracy (ACC) value, and F1 score are the maximum, reaching 95.6%, 88.6%, and 88.9%, respectively. Besides, the root mean square error (RMSE) value is the minimum, namely 0.287, which indicates that stacking ensemble learning including multiple heterogeneous learners is a high-performance model for debris-flow susceptibility assessment. The findings can provide a scientific basis for the disaster prevention and mitigation in the mountainous areas.

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Section: Debris-flow Susceptibility Assessment Unitmentioning

confidence: 99%