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Open AccessArticle

Identification of the Debris Flow Process Types within Catchments of Beijing Mountainous Area

1,2, 1,2,3,4,*, 1,4,5, 1,2 and 6
1
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, China
4
Collaborative Innovation Center of South China Sea Studies, Nanjing 210093, China
5
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
6
Research Institute of Exploration and Development Dagang Oil Field, Tianjin 300280, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(4), 638; https://doi.org/10.3390/w11040638
Received: 27 February 2019 / Revised: 20 March 2019 / Accepted: 22 March 2019 / Published: 27 March 2019
(This article belongs to the Section Water Resources Management and Governance)
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PDF [22978 KB, uploaded 27 March 2019]
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Abstract

The distinguishable sediment concentration, density, and transport mechanisms characterize the different magnitudes of destruction due to debris flow process (DFP). Identifying the dominating DFP type within a catchment is of paramount importance in determining the efficient delineation and mitigation strategies. However, few studies have focused on the identification of the DFP types (including water-flood, debris-flood, and debris-flow) based on machine learning methods. Therefore, while taking Beijing as the study area, this paper aims to establish an integrated framework for the identification of the DFP types, which consists of an indicator calculation system, imbalance dataset learning (borderline-Synthetic Minority Oversampling Technique (borderline-SMOTE)), and classification model selection (Random Forest (RF), AdaBoost, Gradient Boosting (GBDT)). The classification accuracies of the models were compared and the significance of parameters was then assessed. The results indicate that Random Forest has the highest accuracy (0.752), together with the highest area under the receiver operating characteristic curve (AUROC = 0.73), and the lowest root-mean-square error (RMSE = 0.544). This study confirms that the catchment shape and the relief gradient features benefit the identification of the DFP types. Whereby, the roughness index (RI) and the Relief ratio (Rr) can be used to effectively describe the DFP types. The spatial distribution of the DFP types is analyzed in this paper to provide a reference for diverse practical measures, which are suitable for the particularity of highly destructive catchments. View Full-Text
Keywords: debris flow process; machinelearning; catchment; Beijing mountainous area debris flow process; machinelearning; catchment; Beijing mountainous area
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, N.; Cheng, W.; Zhao, M.; Liu, Q.; Wang, J. Identification of the Debris Flow Process Types within Catchments of Beijing Mountainous Area. Water 2019, 11, 638.

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