the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Jan 2026
Integrating multidimensional factors through Bayesian Belief Networks for landslide and debris-flow risk reduction in subtropical zones
Abstract. Current forecasting models for landslides and debris flows mostly look at environmental or socio-economic factors on their own. They rarely combine both into a single probabilistic framework that might give warning in complicated and uncertain situations. This constraint is especially clear in Vietnam, where intense subtropical rain, steep and extensively dissected mountainous terrain, and quick changes in land use and infrastructure are the main causes of landslides and debris flows. This research introduces a novel approach using a Bayesian Belief Network (BBN) to enhance landslide-risk prediction through the integrated analysis of environmental and socioeconomic data. The developed BBN model incorporates inputs from diverse sources, including Geographic Information Systems (GIS), remote sensing, and field survey observations. Structural Equation Modeling was employed to align the BBN with established relationships between landslides and influencing factors. The analysis explored different scenarios by combining rainfall intensity with land-use patterns and assessing the protective role of embankments. Results indicate that precipitation exceeding 130 mm over a period longer than three days markedly increases the likelihood of landslides and debris flows, particularly in agricultural regions. Gabion embankments were found to be highly effective in mitigating risks to both human safety and built environments.
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Status: open (until 19 May 2026)
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RC1: 'Comment on egusphere-2026-160', Anonymous Referee #1, 17 Feb 2026
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CC2: 'Reply on RC1', Hieu Nguyen, 21 Apr 2026
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Letter to editors and reviewers:
Dear editors and reviewers,
Thank you very much for your useful comments, we are now pleased to resubmit the revised version of Egusphere-2026-160 title: “Integrating multidimensional factors through Bayesian Belief Networks for landslide and debris-flow risk reduction in subtropical zones”. Based on the comment of reviewer, the authors changed the title to: “Integrating multidimensional factors through Bayesian Belief Networks for landslide risk reduction in subtropical zones”.
We would like to thank the reviewers for careful and thorough reading of this manuscript and for your suggestions, which helped us to improve the manuscript. We have carefully considered all the suggested changes and revised the manuscript accordingly.
Please refer to "Detailed response to reviewers' comments" below for studying the changes.
Yours Sincerely,
On behalf of all authors who read and agreed on the revised manuscript.
Nguyen Hieu
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CC4: 'Reply on CC2', Hieu Nguyen, 21 Apr 2026
reply
Dear editors and reviewers,
Because the authors do not know how to upload the revised manuscript, we uploaded it here in the "reply" option.
Thank you so much for your understanding.
Best regards,
Nguyen Hieu
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CC4: 'Reply on CC2', Hieu Nguyen, 21 Apr 2026
reply
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CC2: 'Reply on RC1', Hieu Nguyen, 21 Apr 2026
reply
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CC1: 'Comment on egusphere-2026-160', Muhammad Yasir, 19 Apr 2026
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I have carefully read your manuscript. This paper proposes a suitable and meaningful approach to landslide risk assessment in subtropical regions. The integration of multiple data sources in the BBN model, supported by SEM, demonstrates a sound scientific basis and potential for practical application. Results such as rainfall thresholds and the role of reinforcement structures are valuable references for risk management.
Overall, the study meets the requirements and can be considered acceptable after revisions to clarify the novelty of the method, the role of the components in the model, and enhance the interpretation of the results.
- The concept of “multidimensional factors” needs to be more clearly defined from the outset, including the scope of variable groups and selection criteria, to avoid the perception of the concept being purely qualitative.
- This study should consider grouping landslides and mudflows together, collectively referred to as landslides. Because mudflows are essentially a type of landslide, while mudslides need to be considered a separate category. - The choice of the BBN model is reasonable, but the presentation doesn't clearly explain why this method is preferred over other popular machine learning models like Random Forest or Neural Networks. The author only needs to add clarification on the advantages of BBN, such as its ability to handle uncertainty, integrate expert knowledge, or build scenarios, thereby helping readers better understand the basis for choosing the method. The combination of BBN and SEM is a strength, but the specific role of SEM in the network structure construction process (e.g., determining causal relationships or parameter adjustment) needs to be clarified to avoid misunderstandings about the level of integration between the two methods.
- Although the paper emphasizes the integration of socio-economic factors, the specific role of this group of factors is not clearly demonstrated in the results section. The author could add a brief statement to clarify the extent of the influence of these factors in the model, thereby highlighting the significance of the multidimensional approach the study aims for.
- The scenario analysis is a strong point; however, the logic behind constructing the scenarios (combining rainfall, land use, and protective structures) is not clearly explained. Adding a short paragraph explaining the reasons for choosing these combinations would help readers understand the representativeness and practical significance of the constructed scenarios.
- The role of reinforcement structures such as gabion embankments is presented in a very positive light. To achieve better academic balance, the author could add a brief statement about the application conditions, scope of effectiveness, or potential limitations (e.g., cost, suitable terrain), thereby making the conclusions more objective.
- One of the advantages of BBN is its ability to handle uncertainty, but this aspect is not clearly emphasized in the methods section. Adding a sentence explaining how the model reflects and propagates uncertainty would further highlight the advantages of the methodology used in the study.
- Field data is mentioned as an important input source; however, its specific role in the model (calibration, validation, or structural construction) is not clearly presented. The author could clarify this point with a brief statement, thereby increasing the reliability and transparency of the study.
- The conclusion currently mainly summarizes the results but does not truly emphasize a key scientific message. The author could add a highly generalized summary sentence clarifying the core contribution of the study to the field of landslide and mudflow risk assessment, thereby increasing the academic weight of the paper.
Citation: https://doi.org/10.5194/egusphere-2026-160-CC1 -
CC3: 'Reply on CC1', Hieu Nguyen, 21 Apr 2026
reply
Letter to editors and reviewers:
Dear editors and reviewers,
Thank you very much for your useful comments, we are now pleased to resubmit the revised version of Egusphere-2026-160 title: “Integrating multidimensional factors through Bayesian Belief Networks for landslide and debris-flow risk reduction in subtropical zones”. Based on the comment of the first reviewer, the authors changed the title to: “Integrating multidimensional factors through Bayesian Belief Networks for landslide risk reduction in subtropical zones”.
We would like to thank the reviewers for careful and thorough reading of this manuscript and for your suggestions, which helped us to improve the manuscript. We have carefully considered all the suggested changes and revised the manuscript accordingly.
Please refer to "Detailed response to reviewers' comments" below for studying the changes.
Yours Sincerely,
On behalf of all authors who read and agreed on the revised manuscript.
-
CC5: 'Reply on CC1', Hieu Nguyen, 21 Apr 2026
reply
Dear editors and reviewers,
Because the authors do not know how to upload the revised manuscript, we uploaded it here in the "reply" option.
Thank you so much for your understanding.
Best regards,
Nguyen Hieu
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CC3: 'Reply on CC1', Hieu Nguyen, 21 Apr 2026
reply
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Dear Authors,
I have carefully read your manuscript "Integrating multidimensional factors through Bayesian Belief Networks for landslide and debris-flow risk reduction in subtropical zones". Unfortunately, I have identified several critical issues and shortcomings throughout the manuscript that, in my opinion, make it unsuitable for publication in its current form.
A major concern relates to discrepancies and confusion in the terminology. Based on widely accepted classification schemes, debris flows are a type of landslide. However, the manuscript differentiates two types of phenomena: landslides and debris flows. What do the Authors refer to when using the term landslides? Besides debris flows, what types of landslides does this manuscript address? I think this choice/description should be included in the Introduction or in another section.
A further significant issue is related to the term risk, which appears to be both overused and, in several instances, misapplied. According to Varnes et al. (1984), landslide risk is the expected number of lives lost, persons injured, damage to properties and disruption of economic activity due to a particular phenomenon (in this case, landslides) for a given area and time period. That said, I have found that Authors often use risk as a synonym for the likelihood of landslides. Notably, this probability represents hazard, which is only one component of risk (as correctly illustrated in Figure 4). I strongly suggest the Authors to carefully revise the terminology as they are presenting a research paper.
Another shortcoming lies in how the manuscript describes the novelty and the results. In all fairness, I consider the purpose of boiling down the risk assessment into a single statistical model quite interesting. However, by reading the paper, I had the impression that this objective has not been fully achieved. Specifically, this lack is largely due to terminological inconsistencies and insufficient clarity in both the methodological description and the presentation of results. For instance, if the study aims to produce a landslide risk analysis, why is the well-established risk equation never mentioned in the text? This omission is particularly "strange" as Figure 4 clearly displays risk as the main objective. Furthermore, it remains unclear whether vulnerability and exposure were actually assessed. How were they assessed? The manuscript lacks a description of this part. Both vulnerability and exposure should be analysed differently depending on the type of element at risk.
The outcomes are also not sufficiently supported. As currently presented, the results may appear somewhat predictable. For example (line 373-374): " the higher the perceived rainfall and property risks, the higher the perceived landslide risk" or (line 387) "that weather conditions are an important factor in risk assessment and mitigation". These statements describe relationships that are largely expected. Expanding the supporting analysis would make their contribution clearer.
Ultimately, there is significant confusion regarding the form and scale of the results. Given the landslide context, I would have expected a cartographic translation of the outcomes displaying the spatial distribution of hazard/risk across the study area. CInstead, the results are roughly presented as tables or diagrams. This form limits their interpretability.
For the above reasons, I regret to say that I suggest rejection in its current form. Below, I provide further comments that I hope will help Authors improve the overall quality of their work. Good luck!