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Water
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Flowing Mechanism of Debris Flow and Engineering Mitigation
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Flowing Mechanism of Debris Flow and Engineering Mitigation

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 1950

Special Issue Editor

Prof. Dr. Weijie Zhang

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Hohai University, Nanjing, China
Interests: extreme rainfall; water–soil coupling; debris flow; numerical simulation; impact force; machine learning; artificial neural network; risk assessment; mitigation measure

Special Issue Information

Dear Colleagues,

With the development of the economy and society, the impact of human activities on the climate has become increasingly intense. As a consequence, extreme rainfall events frequently occur all over the world, triggering geohazards that bring great suffering to humans and lead to the loss to their belongings. Debris flow, as a water-induced geohazard, has attracted increasing attention worldwide, and research has been carried out for the purposes of preventing disasters and reducing damages.

Therefore, this Special Issue aims to present original research and review articles that discuss slope stability under rainfall, the water–soil coupling mechanism in flow, numerical modeling and artificial intelligence prediction of debris flow, influence range evaluation and risk assessment, engineering mitigation measures, etc.

Potential topics include, but are not limited to, the following:

  1. Stability and failure mechanism of slope under a rainfall event;
  2. Water–soil coupling in debris flow;
  3. Numerical modeling of debris flow;
  4. Artificial intelligence in predicting debris flow;
  5. Influence range evaluation and risk assessment;
  6. Engineering measures to prevent and mitigate the disaster of debris flow.

We look forward to receiving your contributions.

Prof. Dr. Weijie Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • extreme rainfall
  • water–soil coupling
  • debris flow
  • numerical simulation
  • impact force
  • machine learning
  • artificial neural network
  • risk assessment
  • engineering mitigation

Published Papers (3 papers)

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Research

15 pages, 1695 KiB  
Article
Monitoring and Early Warning Method of Debris Flow Expansion Behavior Based on Improved Genetic Algorithm and Bayesian Network
by Jun Li, Javed Iqbal Tanoli, Miao Zhou and Filip Gurkalo
Water 2024, 16(6), 908; https://0-doi-org.brum.beds.ac.uk/10.3390/w16060908 - 21 Mar 2024
Viewed by 561
Abstract
Based on an improved genetic algorithm and debris flow disaster monitoring network, this study examines the monitoring and early warning method of debris flow expansion behavior, divides the risk of debris flow disaster, and provides a scientific basis for emergency rescue and post-disaster [...] Read more.
Based on an improved genetic algorithm and debris flow disaster monitoring network, this study examines the monitoring and early warning method of debris flow expansion behavior, divides the risk of debris flow disaster, and provides a scientific basis for emergency rescue and post-disaster recovery. The function of the debris flow disaster monitoring network of the spreading behavior disaster chain is constructed. According to the causal reasoning of debris flow disaster monitoring information, the influence factors of debris flow, such as rainfall intensity and duration, are selected as the inputs of the Bayesian network, and the probability of a debris flow disaster is obtained. The probability is compared with the historical data threshold to complete the monitoring and early warning of debris flow spreading behavior. Innovatively, by introducing niche technology to improve traditional genetic algorithms by learning Bayesian networks, the optimization efficiency and convergence speed of genetic algorithms are improved, and the robustness of debris flow monitoring and warning is enhanced. The experimental results show that this method divides debris flow disasters into the following five categories based on their danger: low-risk area, medium-risk area, high-risk area, higher-risk area, and Very high-risk area. It accurately monitors the expansion of debris flows and completes early warning. The disaster management department can develop emergency rescue and post-disaster recovery strategies based on early warning results, thus providing a scientific basis for debris flow disasters. The improved genetic algorithm has a higher learning efficiency, a higher accuracy, a faster convergence speed, and higher advantages in learning time and accuracy of the Bayesian network structure. Full article
(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
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16 pages, 2074 KiB  
Article
Multi-Index Fusion Debris Flow Early Warning Model Based on Spatial Interpolation and Support Vector Machine
by Rui Jin, Shaoqi Wang and Jianfei Liu
Water 2024, 16(5), 724; https://0-doi-org.brum.beds.ac.uk/10.3390/w16050724 - 28 Feb 2024
Viewed by 577
Abstract
Debris flow early warning is an effective method to prevent major disasters, so a multi-index fusion debris flow early warning model based on spatial interpolation and a support vector machine is designed. Aiming at the discrete rainfall data in the study area, the [...] Read more.
Debris flow early warning is an effective method to prevent major disasters, so a multi-index fusion debris flow early warning model based on spatial interpolation and a support vector machine is designed. Aiming at the discrete rainfall data in the study area, the collaborative Kriging spatial interpolation method based on Kriging spatial interpolation is adopted to process the rainfall data into multi-index fused surface data. The rainfall data after spatial interpolation are used as the input sample data of the support vector machine early warning model, and the optimal parameters of the support vector machine are calculated by the sea squirt algorithm, and then the debris flow early warning results are output. After experimental analysis, the model can obtain rainfall surface data. After calculation by the model, the accuracy of the early warning probability of debris flow is improved, and the early warning result is consistent with the actual result of debris flow. Full article
(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
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17 pages, 2169 KiB  
Article
Risk Zoning Method of Potential Sudden Debris Flow Based on Deep Neural Network
by Qinglun Xiao, Shaoqi Wang, Na He and Filip Gurkalo
Water 2024, 16(4), 518; https://0-doi-org.brum.beds.ac.uk/10.3390/w16040518 - 06 Feb 2024
Viewed by 620
Abstract
With the continuous increase in global climate change and human activities, the risk of sudden debris flow disasters is becoming increasingly severe. In order to effectively evaluate and zone the potential hazards of debris flows, this paper proposes a method for zoning the [...] Read more.
With the continuous increase in global climate change and human activities, the risk of sudden debris flow disasters is becoming increasingly severe. In order to effectively evaluate and zone the potential hazards of debris flows, this paper proposes a method for zoning the potential sudden hazards of debris flows based on deep neural networks. According to hazard identification, ten risk indicators of potential sudden debris flows are determined. The risk indicators of a potential sudden debris flow in each region were used as the input factors of a deep trust network (DBN) composed of a back propagation (BP) neural network and a restricted Boltzmann machine (RBM). The DBN is pre-trained using the contrast divergence method to obtain the optimal value of the parameter set of the DBN model, and a BP network is set at the last layer of the DBN for fine-tuning to make the network optimal. Using the DBN model with the best parameters, the risk probability of debris flows corresponding to each region is taken as an output. The risk grade is divided, the risk degree of potential sudden debris flow in each region is analyzed, and the potential sudden debris flow risk in each region is divided individually. The results show that this method can effectively complete the risk zoning of sudden debris flow. Moreover, the cumulative contribution of the indicators selected by this method is significant, and the correlation of indicators is not significant, which can play a role in the risk assessment of potential sudden debris flow. This study not only provides new ideas and methods for risk assessment of sudden debris flow disasters, but also fills a gap in the field of geological hazard susceptibility mapping. Full article
(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
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