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A method to increase the number of positive samples for machine learning-based urban waterlogging susceptibility assessments

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Abstract

The frequent occurrence of urban waterlogging seriously affects people’s lives and the national economy. The use of machine learning (ML) methods to spatially assess urban waterlogging susceptibility is critical for reducing the losses caused by such disasters. It is important to select an equal number of positive and negative samples to train binary ML classifiers for evaluation; in most cases, researchers are only able to obtain a relatively small number of historical waterlogging locations (positive samples), which leads to the selection of a limited number of negative samples, further affecting the trained classifiers’ performance. Facing this issue, we proposed an optimized seed spread algorithm (OSSA) that can estimate the potential inundated areas based on the spatial distribution of elevation and natural waters, thereby increasing the number of positive samples. The primary urban area of Guangzhou, China, was selected as the study region, and random forest was selected as the evaluation algorithm. We further employed two ML methods, support vector machine and logistic regression, to verify the quality of the increased positive samples. The results indicate that compared with the original positive samples, the OSSA-based positive samples achieve the highest area under the curve values among the three tested ML methods, indicating that the OSSA can be a suitable approach to increase the number of positive samples for such studies. We believe that this study advances the ML-based waterlogging susceptibility assessments, which could be valuable for developing countries where intensive hydrologic monitoring is lacking.

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Data availability

The data of this study is available at the permanent link: https://figshare.com/articles/dataset/codes_data/13286099.

Code availability

The codes of RF, a source which is opening to public, are available in GitHub at https://github.com/belindee/RandomForcastToiletries. The codes of LR are built-in functions in MATLAB 2017a. The codes of SVM and PSO are available in GitHub at: https://github.com/faruto/Libsvm-FarutoUltimate-Version. The codes of OSSA, developed in this study, are available at the permanent link: https://figshare.com/articles/dataset/codes_data/13286099.

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Acknowledgements

We express our sincere thanks to Dr. George Christakos, SI Guest editor, and three anonymous reviewers, as their valuable comments and suggestions greatly improved the quality of the paper. We express our sincere thanks to Guibing Zheng, for his assistance related to data collection during preparation of this manuscript. We thank American Journal Experts for its linguistic assistance during preparation of this manuscript.

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Authors and Affiliations

  1. Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan

    Xianzhe Tang

  2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, Jiangsu, China

    Jiufeng Li

  3. School of Geography, South China Normal University, Guangzhou, 510631, China

    Wei Liu

  4. School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China

    Huafei Yu

  5. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

    Fangfang Wang

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  1. Xianzhe Tang
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Contributions

Xianzhe Tang: Software, Writing—original draft, Validation, Methodology, Conceptualization, Formal analysis. Jiufeng Li: Validation, Writing—review & editing, Formal analysis. Wei Liu: Resources, Supervision, Validation, Writing—review & editing, Formal analysis. Huafei Yu: Writing—review & editing. Fangfang Wang: Writing—review & editing.

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Correspondence to Wei Liu.

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Tang, X., Li, J., Liu, W. et al. A method to increase the number of positive samples for machine learning-based urban waterlogging susceptibility assessments. Stoch Environ Res Risk Assess 36, 2319–2336 (2022). https://doi.org/10.1007/s00477-021-02035-8

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