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Monitoring, Modelling, Assessment and Mitigation of Debris Flow Hazards
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Monitoring, Modelling, Assessment and Mitigation of Debris Flow Hazards

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

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 3859

Special Issue Editors

Prof. Dr. Yu Huang

E-Mail Website
Guest Editor
Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, China
Interests: geological disaster; engineering geology; computational geomechanics; geotechincial engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Dianlei Feng

E-Mail Website
Guest Editor
Department of Hydraulic Engineering, College of Civil Engineering, Tongji University, Shanghai, China
Interests: geo-harzard; numerical modeling; CFD; meshfree methods; porous media; fluid-structure interaction
Dr. Xingyue Li

E-Mail Website
Guest Editor
Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, China
Interests: gravitational mass movement; snow avalanche; debris flow; numerical simulation; CFD-DEM; MPM

Special Issue Information

Dear Colleagues,

Debris flow hazards represent dynamic and devastating natural phenomena that pose significant risks to communities, infrastructure, and ecosystems. These events occur when large volumes of water, sediment, and debris cascade rapidly down steep slopes, triggered by intense rainfall, snowmelt, volcanic eruptions, seismic activities, or a combination of these factors. The consequences of debris flows can be catastrophic, resulting in loss of life, severe property damage, disruption of essential services, and adverse environmental impacts.

Addressing the complexities of debris flow hazards requires a multidimensional approach that encompasses rigorous monitoring, advanced numerical modelling, comprehensive assessment, and effective mitigation strategies. Timely and accurate monitoring is vital to track debris flow dynamics, spatial extent, and the factors influencing their initiation and propagation. Coupled with advanced numerical modelling, these insights enhance our ability to forecast debris flow behavior and assess potential consequences. Comprehensive assessment is essential to understand the full scope of the disaster's impact, including its effects on communities, infrastructure, and the environment, thereby informing targeted mitigation measures.

Scope and Objectives:

This Special Issue on “Monitoring, Modelling, Assessment and Mitigation of Debris Flow Hazards” aims to be a comprehensive platform that explores innovative approaches, methodologies, technologies, and case studies related to debris flow hazards in diverse geographical settings. This Special Issue will cover a wide range of research topics, including monitoring techniques, advanced numerical modelling, hazard assessment, vulnerability analysis, mitigation strategies, and lessons from historical events.

The primary objectives of this Special Issue include:

  • Advancements in Monitoring Techniques: Presenting novel approaches in real-time monitoring, sensor networks, remote sensing, and geographic information systems (GISs) to capture essential data on debris flow initiation, progression, and potential hazards.
  • Numerical Modelling of Debris Flow Behavior: Showcasing innovative numerical modeling techniques to simulate and predict debris flow movement, improving forecasting accuracy and risk assessment.
  • Hazard Assessment and Vulnerability Analysis: Investigating methodologies for assessing the vulnerability of regions to debris flow hazards, identifying high-risk areas, and evaluating potential impacts on critical infrastructure, settlements, and natural systems.
  • Mitigation Strategies and Early Warning Systems: Highlighting effective mitigation measures and early warning systems to alert at-risk populations and authorities, enhancing disaster preparedness and response.
  • Environmental Implications: Examining the far-reaching consequences of debris flows on aquatic ecosystems, water quality, sediment transport, and downstream effects on rivers and coastal areas.
  • Lessons from Historical Events: Drawing insights from past debris flow disasters and case studies to improve understanding, identify common patterns, and guide best practices for monitoring, numerical modeling, assessment, and mitigation.

Prof. Dr. Yu Huang
Dr. Dianlei Feng
Dr. Xingyue Li
Guest Editors

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

  • debris flow
  • natural hazards
  • disaster monitoring
  • disaster evaluation
  • numerical modeling
  • remote sensing
  • climate change
  • forecasting models
  • real-time data collection

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Published Papers (2 papers)

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19 pages, 4677 KiB  
Article
Optimization of Injection Methods in the Microbially Induced Calcite Precipitation Process by Using a Field Scale Numerical Model
by Lingxiang Wang, Huicao Shao, Can Yi, Yu Huang and Dianlei Feng
Water 2024, 16(1), 82; https://doi.org/10.3390/w16010082 - 25 Dec 2023
Cited by 1 | Viewed by 1536
Abstract
Microbially induced calcite precipitation (MICP) is a promising, more eco-friendly alternative method for landslide prevention and foundation reinforcement. In this study, we investigated the optimization of injection methods within the MICP process in porous media to enhance calcite mass and consolidation effect. The [...] Read more.
Microbially induced calcite precipitation (MICP) is a promising, more eco-friendly alternative method for landslide prevention and foundation reinforcement. In this study, we investigated the optimization of injection methods within the MICP process in porous media to enhance calcite mass and consolidation effect. The results demonstrated that staged injections with considerable advantages significantly improved precipitated calcite mass by 23.55% compared with continuous injection methods. However, extended retention times in staged injections reduced reinforcement effects. Moreover, setting the additional time in all injection methods can improve the consolidation area and effect without added injections. Apart from the injection methods, the changes in porosity and substance concentration also directly affected calcite masses and the reinforcement effect. Both the total calcite mass and the reinforcement effect should be taken into account when selecting appropriate injection methods. In terms of influencing factors on the total calcite mass, substance concentration ≫ average porosity ≫ additional time > retention time in staged injection. For the consolidation effect, substance concentration ≫ retention time in staged injection > average porosity ≫ additional time. The 5 h retention time in staged injections was recommended as the optimum injection method in the geotechnical conditions for average porosity from 0.25 to 0.45, with the changes in different reactant concentrations. Full article
(This article belongs to the Special Issue Monitoring, Modelling, Assessment and Mitigation of Debris Flow Hazards)
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Review

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23 pages, 10917 KiB  
Review
Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis
by Lianbing Yang, Yonggang Ge, Baili Chen, Yuhong Wu and Runde Fu
Water 2024, 16(7), 923; https://doi.org/10.3390/w16070923 - 22 Mar 2024
Viewed by 1689
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Monitoring, Modelling, Assessment and Mitigation of Debris Flow Hazards)
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