Structured exploration of machine learning model complexity for spatio-temporal forecasting of urban flooding

Agonafir, Candace; Zheng, Tian

doi:https://doi.org/10.5194/egusphere-2024-551

Preprints

https://doi.org/10.5194/egusphere-2024-551

Preprints

15 Mar 2024

| 15 Mar 2024

Structured exploration of machine learning model complexity for spatio-temporal forecasting of urban flooding

Candace Agonafir and Tian Zheng

Abstract. Urban flooding may lead to significant socio-economic impacts and loss of life. To afford preventative actions, researchers have implemented various modeling techniques to gain insight into urban flood occurrences. Using New York City (NYC) as the study area, data-driven techniques, specifically statistical and neural network models with increasing spatio-temporal complexity, are formulated and tested, assessing the potential relative contribution of different modeling constructs. Zones, based on flood characteristics, are first delineated using the unsupervised machine learning technique of spectral clustering. Then, the models are applied to each cluster, with comprehensive performance evaluation, as to understand which algorithmic, structural aspects contribute to the reduction of prediction errors. A chief discovery of this study is the emergence of the Graph Wavenet (GWN) as the most effective model due to its proficiency in capturing spatio-temporal aspects and implementing dynamic graph creation. Furthermore, it is seen that the enhancement of specific temporal and spatial components within a modeling technique proves beneficial, and a novel adoption of graph-based architectures is additive. Offering a unique exploration of spatio-temporal aspects, emphasizing the benefits of component enhancement and the adoption of graph-based architectures, this paper identifies modification techniques, which would allow for insights to prevail in urban flood modeling despite being confronted with limited data availability.

Received: 02 Mar 2024 – Discussion started: 15 Mar 2024

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

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Candace Agonafir and Tian Zheng

Status: closed

RC1:
'Comment on egusphere-2024-551', Anonymous Referee #1, 14 Apr 2024

This manuscript attempted to address a crucial topic in urban hydrological modelling: the application of advanced ML algorithms for more accurate urban flood modelling. Although the subject aligns well with HESS's scope, this manuscript requires significant improvements to be considered for publication in HESS, particularly in the following areas:
- The writing is lengthy and lacks focus; much of the text in the methodology re-introduces well-known algorithms. This section could be condensed to allow more space for discussing graph-related algorithms. Currently, it reads more like a degree thesis than a research paper—a point that should be addressed if resubmission is planned.

- While the graph-based algorithm presents an interesting contribution from this work, its configuration is overly simplistic and thus questionable regarding its utility. Specifically, considering only six nodes limits its applicability in real-world settings—why not use the 59 DISs as basic nodes to construct the graph?

- The results presented are somewhat superficial: an essential aspect of such hydrological modelling—the performance during peak times and volumes of floods—is missing. Instead, only several R values from different models are provided—far from satisfactory.
As such, I suggest rejecting this manuscript in its current form.

Citation: https://doi.org/10.5194/egusphere-2024-551-RC1
- AC1:
  'Reply on RC1', Candace Agonafir, 22 Apr 2024
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-551/egusphere-2024-551-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-551-AC1
  - AC2: 'Reply on AC1', Candace Agonafir, 22 Apr 2024
    
    “The writing is lengthy and lacks focus; much of the text in the methodology re-introduces well-known algorithms. This section could be condensed to allow more space for discussing graph-related algorithms. Currently, it reads more like a degree thesis than a research paper—a point that should be addressed if resubmission is planned."
    Thank you for your comments regarding the length and focus of the methodology section in our manuscript. We appreciate your suggestion to condense this section. We agree that the methodology section can be made more concise. However, I would like to clarify the rationale behind the detailed description of well-known algorithms such as FFN, CNN, and RNN in our methodology. Our intent was to provide a foundation that would not require reiteration when discussing the innovative aspects of the Graph WaveNet (GWN) model. This approach was aimed at keeping the subsequent sections focused primarily on the novel graph architecture and its integration with these base models.
    
    “While the graph-based algorithm presents an interesting contribution from this work, its configuration is overly simplistic and thus questionable regarding its utility. Specifically, considering only six nodes limits its applicability in real-world settings—why not use the 59 DISs as basic nodes to construct the graph?"
    
    Thank you for your comment regarding the configuration of the Graph WaveNet (GWN) model and its applicability. Your comment raises an important point about the scale and complexity of the model used in our research.
    In our study, the decision to limit the model to six nodes rather than using the 59 District Indicator Species (DISs) as basic nodes was driven by several critical considerations. First, the GWN model, while complex, is also resource-intensive, requiring significant computational power, memory, and run-time. Implementing a more complex model with 59 nodes would dramatically increase these demands, potentially making the model impractical for the kind of timely and efficient analysis stakeholders aim to achieve.
    Furthermore, the occurrence of street flooding, which is the focus of our study, is relatively infrequent. It does not flood every time it rains. Yet, when it does, it is dangerous and costly. This infrequency limits the amount of data available to train a model at the localized level of 59 DISs effectively. Our chosen approach balances the need for analytical depth with the practical aspects of model training and execution.
    The main contribution of our work lies in its ability to make reasonably accurate predictions in an area that is underexplored in current literature. We focus on both chronic, pluvial flooding and large urban flooding, which pose significant human risks and economic strains. Our model addresses these issues effectively within the constraints imposed by available data and computational resources.
    
    “The results presented are somewhat superficial: an essential aspect of such hydrological modelling—the performance during peak times and volumes of floods—is missing. Instead, only several R values from different models are provided—far from satisfactory.”
    Thank you for your feedback regarding the depth of the results section in our study. We acknowledge your concern about the absence of detailed performance metrics during peak times and volumes of floods. There are no hydrological measurements in NYC and many metropolitan areas. The limitations in available hydrological data have indeed posed a significant challenge. In this context, it is important to highlight that our study relies on crowdsourced data due to the lack of traditional hydrological measurements such as peak times and volumes. Given these constraints, our approach has been to employ data-driven models rather than traditional physics-based models. This choice is driven by the nature of the available data, which does not support the detailed modeling of peak flood conditions. Instead, our model focuses on the relationship between predictor variables and the response variable, which represents areas affected by flooding, to estimate flooding impacts in urban environments. We present graph-based architectures as an innovative solution to address these challenges. This approach is particularly suited to urban terrain scenarios, where internal drainage systems play a significant role, yet detailed drainage data is often unavailable. Our methodology leverages the graph structure to capture the complex interactions within urban areas that influence flooding. The results, while initially seeming straightforward, provide foundational insights into the challenges and potential of modeling urban flooding using novel data sources and computational techniques.
    
    There is utility in a predictive model for an urban area, of which, can create estimates based on predictor variables and their relationship to a response variable representing flooded areas. In the attached pdf, there is an output of the GWN, expressed as percent increase in SF reports (SF complaints on day / average SF complaints) on a rainy day, June 21, 2019. The max hourly rainfall on this day ranged from approximately 8 mm to 10 mm, and the total daily observed amount was roughly 13 mm. Hence, there is intense rainfall for a relatively short duration. On this day, flooding was reported in multiple news outlets.
    Zone 1 experiences the greatest rate of increase, and in this zone, the R² is 0.59. On the other hand, Zone 3, which has the second highest total SF complaints, Zone 3 experiences the lowest rate of increase, and in this zone, the R² is 0.72. This is useful information. It may suggest that the areas of concern for that day [as expressed by the residents] are not necessarily the areas that have been experiencing the most overall flooding over the study period.
    
    Citation: https://doi.org/10.5194/egusphere-2024-551-AC2
RC2:
'Comment on egusphere-2024-551', Anonymous Referee #2, 03 Jun 2024

This paper investigated the AI models with different complexity in modelling urban floods over NYC. The subject clearly fits into the scope of the journal. However, for some reasons, I recommend a decision of Major Revision for this manuscript. Please find my comments in the following. I hope you find them useful.

1. The manuscript needs significant proofreading to improve the readability of the text.

2. The experiment setting is controversial. The GWN is found to have the best R2. In my view, it is not only attributed to the model structure per se (authors mentioned in the manuscript), but also to the additional static attributes. This is unfair for other models as GWN is driven by more relevant features.

3. It is insufficient to evaluate the model performance using a single metric.

4. The Introduction does not sufficiently summarize the current advancements in urban flooding especially for data-driven flood modelling. Additionally, the logic of the current introduction is inadequate. Lines 67-85 seemed to be in the wrong place.

5. Lines 342-355, please separate the result from methodology.

Citation: https://doi.org/10.5194/egusphere-2024-551-RC2
- AC3:
  'Reply on RC2', Candace Agonafir, 17 Jun 2024
  The manuscript needs significant proofreading to improve the readability of the text.
  We thank the reviewer for the valuable feedback and will edit the text to enhance its clarity and readability.
  The experiment setting is controversial. The GWN is found to have the best R2. In my view, it is not only attributed to the model structure per se (authors mentioned in the manuscript), but also to the additional static attributes. This is unfair for other models as GWN is driven by more relevant features.
  We appreciate the reviewer’s observation. The GWN indeed uses static attributes not included in other models. Each tier of our models incorporates additional structure or information about the system. For instance, from GLM to FFN, we employed a neural network to better understand the nonlinear relations between predictors and response. From FFN to RNN and CNN, we added temporal capabilities. From FFN to GCN, we included geospatial connections, and from GCN to GWN, we introduced similarity based on the adjacency matrix, incorporating static attributes.
  Through these models with varying structures, we evaluate the contribution of the information they consider. In the revised manuscript, we will elaborate on this in the discussion where the models are introduced and compared.
  We had included the GCR model in our analysis (lines 730-751), which is essentially the GWN without the adjacency matrix and thus does not use static attributes. The GCR model, comparable to other models not using static variables, outperformed them, including the GCN, due to its dynamic convolutional and recurrent components with skip connections and dilation factors. We will clarify this in the revised manuscript and appreciate the reviewer's suggestion to explain the GWN construction further.
  It is insufficient to evaluate the model performance using a single metric.
  We agree with the reviewer on the importance of evaluating model performance across different aspects of the complex spatiotemporal variation in the data. While we use R² as a measure of prediction skills, Figure 8 visualizes the map of R² for all models, revealing how different models capture spatial heterogeneity in the data. In the revised manuscript, we propose to add plots of autocorrelation of prediction errors to demonstrate how different models account for spatiotemporal dependencies.
  The Introduction does not sufficiently summarize the current advancements in urban flooding especially for data-driven flood modelling. Additionally, the logic of the current introduction is inadequate. Lines 67-85 seemed to be in the wrong place.
  We thank the reviewer for this suggestion. In the revised manuscript, we will provide a brief overview of data-driven flood modeling, especially in urban flooding.
  Our introduction follows the primary aspects of a standard introduction (https://mitcommlab.mit.edu/broad/commkit/journal-article-introduction/):
  General Background: Lines 29-49 introduce urban flooding's occurrence and consequences.
  
  Specific Background: Lines 50-66 discuss modeling endeavors and the need for data-driven techniques.
  
  Our Plan: Lines 67-85 introduce our study's construct and the models explored.
  
  Knowledge Gap: Lines 86-108 discuss previous literature and highlight our study's uniqueness.
  
  Lines 109-117 describe the forthcoming sections.
  
  Lines 342-355, please separate the result from methodology.
  We agree with both reviewers that the methodology section is lengthy. We would like to thank the reviewer for the suggestion on how edit for brevity. These particular lines describe the spectral clustering method. We propose moving the entire spectral clustering section (Lines 309-355) to the Data Preprocessing section after making it more concise.
  
  Citation: https://doi.org/10.5194/egusphere-2024-551-AC3

Status: closed

RC1:
'Comment on egusphere-2024-551', Anonymous Referee #1, 14 Apr 2024

This manuscript attempted to address a crucial topic in urban hydrological modelling: the application of advanced ML algorithms for more accurate urban flood modelling. Although the subject aligns well with HESS's scope, this manuscript requires significant improvements to be considered for publication in HESS, particularly in the following areas:
- The writing is lengthy and lacks focus; much of the text in the methodology re-introduces well-known algorithms. This section could be condensed to allow more space for discussing graph-related algorithms. Currently, it reads more like a degree thesis than a research paper—a point that should be addressed if resubmission is planned.

- While the graph-based algorithm presents an interesting contribution from this work, its configuration is overly simplistic and thus questionable regarding its utility. Specifically, considering only six nodes limits its applicability in real-world settings—why not use the 59 DISs as basic nodes to construct the graph?

- The results presented are somewhat superficial: an essential aspect of such hydrological modelling—the performance during peak times and volumes of floods—is missing. Instead, only several R values from different models are provided—far from satisfactory.
As such, I suggest rejecting this manuscript in its current form.

Citation: https://doi.org/10.5194/egusphere-2024-551-RC1
- AC1:
  'Reply on RC1', Candace Agonafir, 22 Apr 2024
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-551/egusphere-2024-551-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-551-AC1
  - AC2: 'Reply on AC1', Candace Agonafir, 22 Apr 2024
    
    “The writing is lengthy and lacks focus; much of the text in the methodology re-introduces well-known algorithms. This section could be condensed to allow more space for discussing graph-related algorithms. Currently, it reads more like a degree thesis than a research paper—a point that should be addressed if resubmission is planned."
    Thank you for your comments regarding the length and focus of the methodology section in our manuscript. We appreciate your suggestion to condense this section. We agree that the methodology section can be made more concise. However, I would like to clarify the rationale behind the detailed description of well-known algorithms such as FFN, CNN, and RNN in our methodology. Our intent was to provide a foundation that would not require reiteration when discussing the innovative aspects of the Graph WaveNet (GWN) model. This approach was aimed at keeping the subsequent sections focused primarily on the novel graph architecture and its integration with these base models.
    
    “While the graph-based algorithm presents an interesting contribution from this work, its configuration is overly simplistic and thus questionable regarding its utility. Specifically, considering only six nodes limits its applicability in real-world settings—why not use the 59 DISs as basic nodes to construct the graph?"
    
    Thank you for your comment regarding the configuration of the Graph WaveNet (GWN) model and its applicability. Your comment raises an important point about the scale and complexity of the model used in our research.
    In our study, the decision to limit the model to six nodes rather than using the 59 District Indicator Species (DISs) as basic nodes was driven by several critical considerations. First, the GWN model, while complex, is also resource-intensive, requiring significant computational power, memory, and run-time. Implementing a more complex model with 59 nodes would dramatically increase these demands, potentially making the model impractical for the kind of timely and efficient analysis stakeholders aim to achieve.
    Furthermore, the occurrence of street flooding, which is the focus of our study, is relatively infrequent. It does not flood every time it rains. Yet, when it does, it is dangerous and costly. This infrequency limits the amount of data available to train a model at the localized level of 59 DISs effectively. Our chosen approach balances the need for analytical depth with the practical aspects of model training and execution.
    The main contribution of our work lies in its ability to make reasonably accurate predictions in an area that is underexplored in current literature. We focus on both chronic, pluvial flooding and large urban flooding, which pose significant human risks and economic strains. Our model addresses these issues effectively within the constraints imposed by available data and computational resources.
    
    “The results presented are somewhat superficial: an essential aspect of such hydrological modelling—the performance during peak times and volumes of floods—is missing. Instead, only several R values from different models are provided—far from satisfactory.”
    Thank you for your feedback regarding the depth of the results section in our study. We acknowledge your concern about the absence of detailed performance metrics during peak times and volumes of floods. There are no hydrological measurements in NYC and many metropolitan areas. The limitations in available hydrological data have indeed posed a significant challenge. In this context, it is important to highlight that our study relies on crowdsourced data due to the lack of traditional hydrological measurements such as peak times and volumes. Given these constraints, our approach has been to employ data-driven models rather than traditional physics-based models. This choice is driven by the nature of the available data, which does not support the detailed modeling of peak flood conditions. Instead, our model focuses on the relationship between predictor variables and the response variable, which represents areas affected by flooding, to estimate flooding impacts in urban environments. We present graph-based architectures as an innovative solution to address these challenges. This approach is particularly suited to urban terrain scenarios, where internal drainage systems play a significant role, yet detailed drainage data is often unavailable. Our methodology leverages the graph structure to capture the complex interactions within urban areas that influence flooding. The results, while initially seeming straightforward, provide foundational insights into the challenges and potential of modeling urban flooding using novel data sources and computational techniques.
    
    There is utility in a predictive model for an urban area, of which, can create estimates based on predictor variables and their relationship to a response variable representing flooded areas. In the attached pdf, there is an output of the GWN, expressed as percent increase in SF reports (SF complaints on day / average SF complaints) on a rainy day, June 21, 2019. The max hourly rainfall on this day ranged from approximately 8 mm to 10 mm, and the total daily observed amount was roughly 13 mm. Hence, there is intense rainfall for a relatively short duration. On this day, flooding was reported in multiple news outlets.
    Zone 1 experiences the greatest rate of increase, and in this zone, the R² is 0.59. On the other hand, Zone 3, which has the second highest total SF complaints, Zone 3 experiences the lowest rate of increase, and in this zone, the R² is 0.72. This is useful information. It may suggest that the areas of concern for that day [as expressed by the residents] are not necessarily the areas that have been experiencing the most overall flooding over the study period.
    
    Citation: https://doi.org/10.5194/egusphere-2024-551-AC2
RC2:
'Comment on egusphere-2024-551', Anonymous Referee #2, 03 Jun 2024

This paper investigated the AI models with different complexity in modelling urban floods over NYC. The subject clearly fits into the scope of the journal. However, for some reasons, I recommend a decision of Major Revision for this manuscript. Please find my comments in the following. I hope you find them useful.

1. The manuscript needs significant proofreading to improve the readability of the text.

2. The experiment setting is controversial. The GWN is found to have the best R2. In my view, it is not only attributed to the model structure per se (authors mentioned in the manuscript), but also to the additional static attributes. This is unfair for other models as GWN is driven by more relevant features.

3. It is insufficient to evaluate the model performance using a single metric.

4. The Introduction does not sufficiently summarize the current advancements in urban flooding especially for data-driven flood modelling. Additionally, the logic of the current introduction is inadequate. Lines 67-85 seemed to be in the wrong place.

5. Lines 342-355, please separate the result from methodology.

Citation: https://doi.org/10.5194/egusphere-2024-551-RC2
- AC3:
  'Reply on RC2', Candace Agonafir, 17 Jun 2024
  The manuscript needs significant proofreading to improve the readability of the text.
  We thank the reviewer for the valuable feedback and will edit the text to enhance its clarity and readability.
  The experiment setting is controversial. The GWN is found to have the best R2. In my view, it is not only attributed to the model structure per se (authors mentioned in the manuscript), but also to the additional static attributes. This is unfair for other models as GWN is driven by more relevant features.
  We appreciate the reviewer’s observation. The GWN indeed uses static attributes not included in other models. Each tier of our models incorporates additional structure or information about the system. For instance, from GLM to FFN, we employed a neural network to better understand the nonlinear relations between predictors and response. From FFN to RNN and CNN, we added temporal capabilities. From FFN to GCN, we included geospatial connections, and from GCN to GWN, we introduced similarity based on the adjacency matrix, incorporating static attributes.
  Through these models with varying structures, we evaluate the contribution of the information they consider. In the revised manuscript, we will elaborate on this in the discussion where the models are introduced and compared.
  We had included the GCR model in our analysis (lines 730-751), which is essentially the GWN without the adjacency matrix and thus does not use static attributes. The GCR model, comparable to other models not using static variables, outperformed them, including the GCN, due to its dynamic convolutional and recurrent components with skip connections and dilation factors. We will clarify this in the revised manuscript and appreciate the reviewer's suggestion to explain the GWN construction further.
  It is insufficient to evaluate the model performance using a single metric.
  We agree with the reviewer on the importance of evaluating model performance across different aspects of the complex spatiotemporal variation in the data. While we use R² as a measure of prediction skills, Figure 8 visualizes the map of R² for all models, revealing how different models capture spatial heterogeneity in the data. In the revised manuscript, we propose to add plots of autocorrelation of prediction errors to demonstrate how different models account for spatiotemporal dependencies.
  The Introduction does not sufficiently summarize the current advancements in urban flooding especially for data-driven flood modelling. Additionally, the logic of the current introduction is inadequate. Lines 67-85 seemed to be in the wrong place.
  We thank the reviewer for this suggestion. In the revised manuscript, we will provide a brief overview of data-driven flood modeling, especially in urban flooding.
  Our introduction follows the primary aspects of a standard introduction (https://mitcommlab.mit.edu/broad/commkit/journal-article-introduction/):
  General Background: Lines 29-49 introduce urban flooding's occurrence and consequences.
  
  Specific Background: Lines 50-66 discuss modeling endeavors and the need for data-driven techniques.
  
  Our Plan: Lines 67-85 introduce our study's construct and the models explored.
  
  Knowledge Gap: Lines 86-108 discuss previous literature and highlight our study's uniqueness.
  
  Lines 109-117 describe the forthcoming sections.
  
  Lines 342-355, please separate the result from methodology.
  We agree with both reviewers that the methodology section is lengthy. We would like to thank the reviewer for the suggestion on how edit for brevity. These particular lines describe the spectral clustering method. We propose moving the entire spectral clustering section (Lines 309-355) to the Data Preprocessing section after making it more concise.
  
  Citation: https://doi.org/10.5194/egusphere-2024-551-AC3

Candace Agonafir and Tian Zheng

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Short summary

Our study explores the spatio-temporal capabilities of machine learning models in urban flood prediction. First, zones are delineated via spectral clustering. Then, a detailed exploration of statistical and neural network models, each with varying advances, commences. The Graph Wavenet is demonstrated as having the strongest forecasting ability due to its graph architecture and temporal complexities. The research enhances data-driven approaches in urban flood modeling.


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