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Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms

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Abstract

Flood is considered as the most devastating natural hazards that cause the death of many lives worldwide. The present study aimed to predict flood vulnerability for Warsaw, Poland, using three machine learning models, such as the Bayesian logistic regression (BLR), the artificial neural networks (ANN), and the deep learning neural networks (DLNNs). The perfomance of these three methods was assessed in order to select the best method for flood vulnerability mapping in densely urbanized city. Thus, initially, thirteen flood predictors were evaluated using the information gain ratio (IGR), and eight most important predictors were considered from model training and testing. The performance of the applied models and accuracy of the result was evaluated through the area under the curve (AUC) and statistical measures. By using the testing dataset, the result reveals that DLNN (AUC = 0.877) is the more performant model in comparison to ANN (AUC = 0.851) and BLR (AUC = 0.697). However, the BLR model has the lowest predictive capability. The results of the present study could be effectively used for the urban flood management strategies.

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Data availability

The data that support the findings of this study are available from the author [Quoc Bao Pham, quoc_bao.pham@us.edu.pl], upon reasonable request.

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Acknowledgements

Data used in this study were derived from the Polish public administration geoportals (e.g., geoportal.gov.pl, https://geodezja.mazovia.pl/mapy.html#tematyczne) and other open data sources.

Funding

This research was funded by Military University of Technology, Faculty of Civil Engineering and Geodesy (grant number 531-4000-22-785/UGB/2022). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Authors and Affiliations

  1. Department of Geography, Faculty of Science, Aligarh Muslim University (AMU), Aligarh, UP, 202002, India

    Farhana Parvin & Sk Ajim Ali

  2. Institute of Geospatial Engineering and Geodesy, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908, Warsaw, Poland

    Beata Calka & Elzbieta Bielecka

  3. Institute of Applied Technology, Thu Dau Mot University, Binh Duong Province, Vietnam

    Nguyen Thi Thuy Linh

  4. Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska street 60, 41-200, Sosnowiec, Poland

    Quoc Bao Pham

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  1. Farhana Parvin
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  2. Sk Ajim Ali
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  3. Beata Calka
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  4. Elzbieta Bielecka
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  5. Nguyen Thi Thuy Linh
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  6. Quoc Bao Pham
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Contributions

F.P., S.A.A: conceptualization, writing—original draft, software, formal analysis, visualization, BC: data acquisition, analysis, writing, review; EB: data preparation, writing, review; Q.B.P: suppervision, conceptualization, wrting, review

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Correspondence to Nguyen Thi Thuy Linh.

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Parvin, F., Ali, S.A., Calka, B. et al. Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms. Theor Appl Climatol 149, 639–659 (2022). https://doi.org/10.1007/s00704-022-04068-7

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