the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modelling Flood Losses to Microbusinesses in Ho Chi Minh City, Vietnam
Abstract. Microbusinesses are important sources of livelihood for low- and middle-income households. In Ho Chi Minh City (HCMC), Vietnam, many microbusinesses are set up in the ground floor of residential houses susceptible to urban floods. Increasing flood risk in HCMC threatens the financial resources of microbusinesses by damaging business contents and causing business interruption. Since flood loss estimations are rarely conducted at object-level resolution and are often focused on households or large companies, the losses suffered by microbusinesses are often overlooked. This study aims to derive the drivers of flood losses in microbusinesses by applying a Conditional Random Forest to survey data (content losses: n=317; business interruption losses: n=361) collected from microbusinesses in HCMC. The variability of content losses and business interruption were adequately explained by the revenues of the businesses from monthly sales, age of the building where the business is established and water depth in the building during the flood event. Based on the identified drivers, probabilistic loss models (non-parametric Bayesian Networks) were developed using a combination of data-driven and expert-based model formulation. The models estimated the flood losses for HCMC’s microbusinesses with a mean absolute error of 3.8 % for content losses and 18.7 % for business interruption losses. The Bayesian Network model for business interruption performed with a similar predictive performance when it was regionally transferred and applied to comparable survey data from another Vietnamese city, Can Tho. The flood loss models introduced in this study make it possible to derive flood risk metrics specific to microbusinesses to support adaptation decision making and risk transfer mechanisms.
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RC1: 'Comment on egusphere-2024-2340', Anonymous Referee #1, 25 Oct 2024
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Comments on: Modelling Flood Losses to Microbusinesses in Ho Chi Minh City, Vietnam by Buch et al. (2024-2340)
This paper aims to identify the drivers of flood losses in microbusinesses by employing a Conditional Random Forest on survey data collected from microbusinesses in Ho Chi Minh City in Vietnam. Based on the drivers identified, probabilistic loss models (non-parametric Bayesian Networks) were developed using a combination of data-driven and expert based model formulation. The transferability of this models was assessed by applying data from a different city to evaluate their broader applicability.
I have read the paper with great interest, and the main objective addressed by the manuscript is within the scope of the journal. Nevertheless, major revisions are necessary to make a few points clearer and I recommend accepting it only after these revisions.
Major comments:
I believe the paper could benefit from separating the results from the discussion to enhance clarity. As someone without extensive expertise in ML algorithms, I found it challenging at time to connect the information in the figures and tables with the text. For instance, on pages 10 and 11, Figure 3 is only referenced once, and while the authors discuss correlations among variables, they do not always provide specific numerical values from the figures. The paper contains a substantial amount of results, which makes it difficult to easily connect the text with the accompanying figures and tables. Please, check the figures axes names. Sometime you start with capital letter and sometimes with small
Additionally, I recommend simplifying certain figures (e.g. Figure 5) or providing more detailed descriptions of them in the text. Given that this journal focuses on natural hazards research and is accessed by readers who may not be experts in ML algorithms, a clearer structure with separate results and discussion would aid.
In the methodology section, I suggest that the authors provide further clarification in certain areas. For example, in the abstract, content losses are reported 317 and business interruption losses as 361. However, in the section presenting the data is noted that 250 responses were collected resulting in 397 loss records in the HCMC and for Can Tho, responses were received from 373 microbusiness, of which 313 provided information on losses. It is unclear how these numbers were derived and calculated. So, please check the numbers and a more detailed explanation of the methods used would be helpful.
Additionally, presenting the equations for the error formulas mentioned in lines 147–150 would enhance clarity. There are also methods referenced in the results that are not described in the methodology section. For instance, cumulative distribution functions (CDFs) are discussed in line 357, yet they are not explained in the methods. Including these details would improve transparency and ensure a more complete understanding of the approach used.
In the introduction, the authors place emphasis on the case study to motivate the analysis. It may be beneficial to move the detailed description of the case study to a separate section, allowing the introduction to focus more directly on the research gaps. This would help to clearly establish the broader motivation and context for the study before delving into the specifics of the case study.
The discussion and conclusion sections could be enhanced by further exploring how the findings may be utilized by other experts and their implications for flood risk management. Expanding on these aspects would clarify the broader relevance of the outputs.
Citation: https://doi.org/10.5194/egusphere-2024-2340-RC1 -
CC1: 'Reply on RC1', Anna Buch, 28 Oct 2024
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Thank you very much for the helpful comments. We will incorporate them into the manuscript as soon as possible.
Citation: https://doi.org/10.5194/egusphere-2024-2340-CC1
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CC1: 'Reply on RC1', Anna Buch, 28 Oct 2024
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Model code and software
Flood Loss Models for Microbusinesses, Vietnam Anna Buch https://github.com/A-Buch/flood-loss-models-4-HCMC/tree/microbusiness-paper
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