Early Identification of River Blockage Disasters Caused by Debris Flows in the Bailong River Basin, China

Author:

Zeng Jianjun12,Zhao Yan3ORCID,Zheng Jiaoyu4,Zhang Yongjun5,Shi Pengqing5,Li Yajun3,Chen Guan367,Meng Xingmin367ORCID,Yue Dongxia4

Affiliation:

1. College of Urban Environment, Lanzhou City University, Lanzhou 730070, China

2. State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China

3. School of Earth Sciences, Lanzhou University, Lanzhou 730000, China

4. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

5. Geological Environment Monitoring Institute of Gansu Province, Lanzhou 730050, China

6. Gansu Technology & Innovation Center for Environmental Geology and Geohazards Prevention, Lanzhou 730000, China

7. Gansu Geohazards Field Observation and Research Station, Lanzhou 730000, China

Abstract

The Bailong River Basin is one of the most developed regions for debris flow disasters worldwide, often causing severe secondary disasters by blocking rivers. Therefore, the early identification of potential debris flow disasters that may block the river in this region is of great significance for disaster risk prevention and reduction. However, it is quite challenging to identify potential debris flow disasters that may block rivers at a regional scale, as conducting numerical simulations for each debris flow catchment would require significant time and financial resources. The purpose of this article is to use public resource data and machine learning methods to establish a relationship model between debris flow-induced river blockage and key influencing factors, thereby economically predicting potential areas at risk for debris flow-induced river blockage disasters. Based on the field investigation, data collection, and remote sensing interpretation, this study selected 12 parameters, including the basin area, basin height difference, relief ratio, circularity ratio, landslide density, fault density, lithology index, annual average frequency of daily rainfall exceeding 40 mm, river width, river discharge, river gradient, and confluence angle, as critical factors to determine whether debris flows will cause river blockages. A relationship model between debris flow-induced river blockage and influencing factors was constructed based on machine learning algorithms. Several machine learning algorithms were compared, and the XGB model performed the best, with a prediction accuracy of 0.881 and an area under the ROC curve of 0.926. This study found that the river width is the determining factor for debris flow blocking rivers, followed by the annual average frequency of daily rainfall exceeding 40 mm, basin height difference, circularity ratio, basin area, and river discharge. The early identification method proposed in this study for river blockage disasters caused by debris flows can provide a reference for the quantitative assessment and pre-disaster prevention of debris flow-induced river blockage chain risks in similar high-mountain gorge areas.

Funder

Major Scientific and Technological Projects of Gansu Province

Second Tibetan Plateau Scientific Expedition and Research Program

National Natural Science Foundation of China

important talent project of Gansu Province

Key Research and Development Program of Gansu province

Natural Science Foundation of Gansu Province

Construction Project of Gansu Technological Innovation Center

Project of Gansu Provincial Party Committee Organization Department

Higher Education Institutions Research Projects of Gansu Province

Project of Risk Assessment and Comprehensive Prevention-Control Technology for Major Geological Disasters in Zhouqu, Gansu Province

Publisher

MDPI AG

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