Numerical Simulation of Boulder Fluid–Solid Coupling in Debris Flow: A Case Study in Zhouqu County, Gansu Province, China

Wang, Fei; Wang, Jiading; Chen, Xiao Qing; Zhang, Shaoxiong; Qiu, Haijun; Lou, Canyun

doi:10.3390/w14233884

Cited by 7 publications

(4 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…), traditional statistical models (weight of evidence method [37,38], certainty factor [39,40], frequency ratio [40,41], etc. ), numerical simulation models (FLO-2D [42], Flow-3D [43,44], Debris2D [45], etc. ), and ML models (LR [46,47], RF [24,48], convolutional neural networks [49,50], etc.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

Yang,

Ge,

Chen

et al. 2024

Water

View full text Add to dashboard Cite

Machine learning (ML) has become increasingly popular in the prediction of debris flow occurrence, but the various ML models utilized as baseline predictors reported in previous studies are typically limited to individual case bases. A comprehensive and systematic evaluation of existing empirical evidence on the utilization of ML as baseline predictors for debris flow occurrence is lacking. To address this gap, we conducted a meta-analysis of ML-based prediction modeling of debris flow occurrence by retrieving papers that were published between 2000 and 2023 from the Scopus and Web of Science databases. The general findings were as follows: (1) A total of 84 papers, distributed across 37 different journals in this time period, reflecting an overall upward trend. (2) Debris flow disasters occur throughout the world, and a total of 13 countries carried out research on the prediction of debris flow occurrence based on ML; China made significant contributions, but more research efforts in African countries should be considered. (3) A total of 36 categories of ML models were utilized as baseline predictors for debris flow occurrence, with logistic regression (LR) and random forest (RF) emerging as the most popular choices. (4) Feature engineering and model comparison were the most commonly utilized strategies in predicting debris flow occurrence based on ML (53 and 46 papers, respectively). (5) Interpretation methods were rarely utilized in predicting debris flow occurrence based on ML, with only 16 papers reporting their utilization. (6) In the prediction of debris flow occurrence based on ML, interpretation methods were rarely utilized, searching by data materials was the most important sample data source, the topographic factors were the most commonly utilized category of candidate variables, and the area under the ROC curve (AUROC) was the most frequently reported evaluation metric. (7) LR’s prediction performance for debris flow occurrence was inferior to that of RF, BPNN, and SVM; SVM was comparable to RF, and all superior to BPNN. (8) The application process for the prediction of debris flow occurrence based on ML consisted of three main steps: data preparation, model construction and evaluation, and prediction outcomes. The research gaps in predicting debris flow occurrence based on ML include utilizing new ML techniques and enhancing the interpretability of ML. Consequently, this study contributes both to academic ML research and to practical applications in the prediction of debris flow occurrence.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

Yang,

Ge,

Chen

et al. 2024

Water

View full text Add to dashboard Cite

show abstract

“…For example, on January 12, 2019, a landslide disaster occurred in Pinganzhai, Longji Terrace Scenic Area, Guangxi Autonomous Region, China, resulting in one person's death [3]. More than one thousand people, for example, at about 22:00 on August 7, 2010, the town of Zhouqu County, Gannan Tibetan Autonomous Prefecture, Gansu Province, China was attacked by a geological disaster of debris flow, resulting in 1557 deaths and 284 missing [4]. According to the definition of geological hazards in the Regulations on the Prevention and Control of Geological Hazards issued by the State Council of China in 2003, geological hazards are disasters related to geological processes, such as mountain collapse, landslide, debris flow, ground collapse, ground fissure, ground subsidence, etc., which are caused by natural factors or human activities and endanger the safety of people's lives and property [5], and also clearly highlight the important role of human subjects in the hazards of geological hazards.…”

Section: Introductionmentioning

confidence: 99%

Thinking on Effective Methods of Successful Prediction of Geological Hazards in Shaanxi, China

Yao¹,

Li²,

He³

et al. 2023

IJG

View full text Add to dashboard Cite

In the process of human survival and development, it is inevitable to transform the original state of the world, thus forming a contradiction between the earth and the earth. The violent form of this contradiction is geological disasters. Geological disasters pose a threat to human life and property, and cause damage caused by natural or human factors, often causing casualties. The destruction process of geological disasters is usually a gradual process, showing many pre-disaster symptoms, such as local landslides, surface cracks, building deformation, tree skew, and ground sound. Evacuation can be avoided in advance according to the disaster precursors, so as to avoid casualties and achieve successful prediction. By reviewing the general situation of geological disasters in Shaanxi Province and the casualties in 2020, the difficulties in the prevention and control of geological disasters are summarized. In view of these difficulties, an on-site investigation, visit and analysis of geological disaster points and successful forecast points in Shaanxi Province in 2020 were conducted. In addition, combined with actual cases and years of work experience, the successful prediction experience of geological disasters was discussed from 8 aspects. Finally, the "Regulations on the Reward for Successful Geological Disaster Forecasting in Shaanxi Province" was revised in order to improve the successful prediction ability of geological disasters in Shaanxi Province and even the whole country, provide reference for future prevention and control of geological disasters, and effectively protect the safety of people's lives and property.

show abstract

“…Dynamic modeling is one of the current research approaches to predicting debris flow movement and accumulation; it generally employs numerical techniques to simulate debris flow transport and accumulation processes using energy and motion conversion laws (Kang et al, 2022;Wang et al, 2022). For different debris flow dynamics issues, Bao et al (2021) compared and investigated the Eulerian method, the CEL method, and the FEM-SPH method based on the Abaqus software in landslide barrage simulation by applying the method of fluid-solid coupling a coupled fluid-solid coupling (Bao et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Detailed investigation and analysis of the dynamic evolutionary process of rainstorm debris flows in mountain settlements: a case study of Xiangbizui Gully

Cai,

Huang,

Yang

2023

Front. Earth Sci.

View full text Add to dashboard Cite

Short-term heavy rainfall often causes large-scale rainstorm debris flows in mountainous areas of Southwest China. Aiming to investigate the accumulation and movement of potential source material for the formation of debris flow hazards under extreme short-term heavy rainfall, this paper takes the Xiangbizui debris flow gully, Southwest China, as a case study. A detailed field engineering and geological investigation was carried out on the valley characteristics, formation conditions, provenance types, distribution range, loose solid material reserves that can be transformed into debris flows, and characteristics showing the variation in the grain size of the accumulated solids along the gully to further explore the characteristics of rainstorm-induced debris flow movement. The dynamic processes of debris flow movement and accumulation are numerically simulated to analyze the maximum velocity, accumulation height, range of influence, and evolutionary process based on the theory of continuous media of the approximate Voellmy solution and a high-precision three-dimensional model. The results indicated that rainstorms and steep terrain are the main factors stimulating debris flows. The amount of loose solid material in the channel is approximately 1550.61 × 104 m3, and the dynamic material reserves are approximately 396.41 × 104 m3. The maximum flow depth and velocity are approximately 3.5 m/s and 13 m/s, respectively, which mainly occur in the upper and middle reaches of the channel and in the accumulation fan at the outlet of the channel. The evolutionary process of the debris flow includes four stages: a 0–1,500 m initial acceleration stage, a 1,500–2,200 m fast forward movement stage, a 2,200–3,400 m acceleration stage in the middle and lower reaches, and a 3,400–4,300 m deceleration and end of accumulation stage. The research findings can provide a scientific basis and strong support for risk assessment and avoidance, as well as prevention and control of debris flows in mountainous areas with severe climate change.

show abstract

Numerical Simulation of Boulder Fluid–Solid Coupling in Debris Flow: A Case Study in Zhouqu County, Gansu Province, China

Cited by 7 publications

References 53 publications

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

Thinking on Effective Methods of Successful Prediction of Geological Hazards in Shaanxi, China

Detailed investigation and analysis of the dynamic evolutionary process of rainstorm debris flows in mountain settlements: a case study of Xiangbizui Gully

Contact Info

Product

Resources

About