Susceptibility Assessment of Single Gully Debris Flow Based on AHP and Extension Method

Debris flow mainly happens in mountainous areas all around the world with deadly social and economic impacts. With the speedy development of the mountainous economy, the debris flow susceptibility evaluation in the mountainous areas is of crucial importance for the safety of mountainous life and economy. Yunnan province of China is one of the worst hitting areas by debris flow in the world. In this paper, debris flow susceptibility assessment of Datong and Taicun gully near the first bend of Jinsha River has been done with the help of site investigation and GIS and remote sensing techniques. Eight causative factors, including slope, topographic wetness index, sediments transport index, ground roughness, basin area, bending coefficient, source material, and normalised difference vegetation index, have been selected for debris flow susceptibility evaluation. Analytical hierarchy process combined with Extension method has been used to calculate the susceptibility level of Datong and Taicun gullies. The evaluation result shows that both the gullies have a moderate susceptibility to debris flow. The result suggests that all the ongoing engineering projects such as mining and road construction work should be done with all precautionary measures, and the excavated material should adequately store in the gullies. Doi: 10.28991/cej-2021-03091702 Full Text: PDF

Download Full-text

Debris-Flow Susceptibility Assessment Model and Its Application in Semiarid Mountainous Areas of the Southeastern Tibetan Plateau

Natural Hazards Review ◽

10.1061/(asce)nh.1527-6996.0000229 ◽

2017 ◽

Vol 18 (2) ◽

pp. 05016005 ◽

Cited By ~ 7

Author(s):

Jian Chen ◽

Yan Li ◽

Wendy Zhou ◽

Javed Iqbal ◽

Zhijiu Cui

Keyword(s):

Tibetan Plateau ◽

Debris Flow ◽

Assessment Model ◽

Susceptibility Assessment ◽

Mountainous Areas ◽

Southeastern Tibetan Plateau ◽

Debris Flow Susceptibility

Download Full-text

Debris flow susceptibility assessment based on an empirical approach in the central region of South Korea

Geomorphology ◽

10.1016/j.geomorph.2018.01.025 ◽

2018 ◽

Vol 308 ◽

pp. 1-12 ◽

Cited By ~ 10

Author(s):

Sinhang Kang ◽

Seung-Rae Lee

Keyword(s):

South Korea ◽

Debris Flow ◽

Central Region ◽

Empirical Approach ◽

Susceptibility Assessment ◽

Debris Flow Susceptibility

Download Full-text

Debris Flow Susceptibility Assessment and Runout Prediction: A Case Study in Shiyang Gully, Beijing, China

International Journal of Environmental Research ◽

10.1007/s41742-020-00263-4 ◽

2020 ◽

Vol 14 (3) ◽

pp. 365-383 ◽

Cited By ~ 2

Author(s):

Yongchao Li ◽

Jianping Chen ◽

Yiwei Zhang ◽

Shengyuan Song ◽

Xudong Han ◽

...

Keyword(s):

Debris Flow ◽

Susceptibility Assessment ◽

Beijing China ◽

Debris Flow Susceptibility

Download Full-text

Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Sensors ◽

10.3390/s19163451 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3451 ◽

Cited By ~ 8

Author(s):

Usman Salihu Lay ◽

Biswajeet Pradhan ◽

Zainuddin Bin Md Yusoff ◽

Ahmad Fikri Bin Abdallah ◽

Jagannath Aryal ◽

...

Keyword(s):

Data Mining ◽

Debris Flow ◽

Airborne Lidar ◽

Multivariate Adaptive Regression Splines ◽

Slope Aspect ◽

Support Vector ◽

Mountainous Area ◽

Topographic Wetness Index ◽

Conditioning Factors ◽

Debris Flow Susceptibility

Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.

Download Full-text