The Application of a Three-Dimensional Deterministic Model in the Study of Debris Flow Prediction Based on the Rainfall-Unstable Soil Coupling Mechanism

Abstract

As debris flow is one of the most destructive natural disasters in many parts of the world, the assessment and management of future debris flows with proper forecasting methods are crucial for the safety of life and property. So increasing attention has been paid to the forecasting methods on debris flows. A debris flow forecasting method based on the rainfall-unstable soil coupling mechanism (R-USCM) is presented in the current study. This method is based on the debris flow formation mechanism. The density of sediment is introduced as an evaluation index to determine the susceptibility of debris flow occurrence. The forecasting method includes two phases: (1) rainfall and soil coupling and (2) runoff and unstable soil coupling. Scoops3D, a three-dimensional (3D) model for analyzing slope stability, was introduced into the debris flow forecasting method. In order to test the forecasting accuracy of this method, Jiaohe County was selected as a research area, and the serious debris flow disasters attributed to strong rainfall on 20 July 2017 were taken as the research case. By comparing the forecasting results with the debris flow distribution map for Jiaohe County, the method based on the R-USCM is feasible for forecasting debris flows at the regional scale. The application of the Scoops3D model can more reasonably analyze the slope stability than the traditional two dimensional (2D) method and improve the forecasting ability of debris flows.

Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

“…According to the collected data and previous studies [48], Jiaohe County is one of the most serious areas with geological disasters in Jilin Province, which poses a direct threat to the life and property of local residents due to the landslides induced by short-term heavy rainfall and excessive human activities. Farmland, roads, and houses in the research area have been seriously damaged ( Figure 2).…”

Optimal Simulation of Sandcastle Life in Dynamic Environment Based on Stability Principle

“…From formula (11) to formula (12), the optimal solution is obtained by calculation: X � 0.63, Y � 0.22.…”

“…e ratio of water to sand will directly affect the antierosion ability of sandcastle foundation. By introducing the relation equation between water-sand polymerization degree and water-sand ratio, we limited the allowable range of polymerization degree to obtain a reasonable range of water-sand ratio and then solved the optimal water-sand ratio through models [11,12].…”

Landslide Detection from Open Satellite Imagery Using Distant Domain Transfer Learning

“…Landslide detection is regarded as the study of satellite image recognition and classification [6]. Machine learning methods, such as support vector machines (SVMs) [12,13], artificial neural networks (ANNs) [14] and deep learning, have been extensively used in landslide research [1,15], especially convolutional neural networks (CNNs), which have become a research focus in the field of landslide detection [16,17]. The SVM, ANN, RF and CNN methods have been used for potential landslide detection using optical remote sensing images and topography [18], and the CNN has been considerably improved as the mainstream deep learning method used to extract information from remote sensing images [19].…”