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Assessment of urban flood susceptibility using semi-supervised machine learning model

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Assessment of urban flood susceptibility using semi-supervised machine learning model. / Zhao, Gang; Pang, Bo; Xu, Zongxue; Peng, Dingzhi; Xu, Liyang.

In: Science of The Total Environment, Vol. 659, 01.04.2019, p. 940-949.

Research output: Contribution to journalArticle

Harvard

Zhao, G, Pang, B, Xu, Z, Peng, D & Xu, L 2019, 'Assessment of urban flood susceptibility using semi-supervised machine learning model' Science of The Total Environment, vol. 659, pp. 940-949. https://doi.org/10.1016/j.scitotenv.2018.12.217

APA

Zhao, G., Pang, B., Xu, Z., Peng, D., & Xu, L. (2019). Assessment of urban flood susceptibility using semi-supervised machine learning model. Science of The Total Environment, 659, 940-949. https://doi.org/10.1016/j.scitotenv.2018.12.217

Vancouver

Author

Zhao, Gang ; Pang, Bo ; Xu, Zongxue ; Peng, Dingzhi ; Xu, Liyang. / Assessment of urban flood susceptibility using semi-supervised machine learning model. In: Science of The Total Environment. 2019 ; Vol. 659. pp. 940-949.

Bibtex

@article{26c697aa4f6846d2a7b95294f204e7c3,
title = "Assessment of urban flood susceptibility using semi-supervised machine learning model",
abstract = "In order to identify flood-prone areas with limited flood inventories, a semi-supervised machine learning model—the weakly labeled support vector machine (WELLSVM)—is used to assess urban flood susceptibility in this study. A spatial database is collected from metropolitan areas in Beijing, including flood inventories from 2004 to 2014 and nine metrological, geographical, and anthropogenic explanatory factors. Urban flood susceptibility is mapped and compared using logistic regression, artificial neural networks, and a support vector machine. Model performances are evaluated using four evaluation indices (accuracy, precision, recall, and F-score) as well as the receiver operating characteristic curve. The results show that WELLSVM can better utilize the spatial information (unlabeled data), and it outperforms all comparison models. The high-quality WELLSVM flood susceptibility map is thus applicable to efficient urban flood management.",
keywords = "Beijing, Flood susceptibility, Semi-supervised machine learning model, Urban area, Weakly labeled support vector machine",
author = "Gang Zhao and Bo Pang and Zongxue Xu and Dingzhi Peng and Liyang Xu",
year = "2018",
month = "12",
day = "15",
doi = "10.1016/j.scitotenv.2018.12.217",
language = "English",
volume = "659",
pages = "940--949",
journal = "Science of The Total Environment",
issn = "0048-9697",
publisher = "Elsevier",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Assessment of urban flood susceptibility using semi-supervised machine learning model

AU - Zhao, Gang

AU - Pang, Bo

AU - Xu, Zongxue

AU - Peng, Dingzhi

AU - Xu, Liyang

PY - 2018/12/15

Y1 - 2018/12/15

N2 - In order to identify flood-prone areas with limited flood inventories, a semi-supervised machine learning model—the weakly labeled support vector machine (WELLSVM)—is used to assess urban flood susceptibility in this study. A spatial database is collected from metropolitan areas in Beijing, including flood inventories from 2004 to 2014 and nine metrological, geographical, and anthropogenic explanatory factors. Urban flood susceptibility is mapped and compared using logistic regression, artificial neural networks, and a support vector machine. Model performances are evaluated using four evaluation indices (accuracy, precision, recall, and F-score) as well as the receiver operating characteristic curve. The results show that WELLSVM can better utilize the spatial information (unlabeled data), and it outperforms all comparison models. The high-quality WELLSVM flood susceptibility map is thus applicable to efficient urban flood management.

AB - In order to identify flood-prone areas with limited flood inventories, a semi-supervised machine learning model—the weakly labeled support vector machine (WELLSVM)—is used to assess urban flood susceptibility in this study. A spatial database is collected from metropolitan areas in Beijing, including flood inventories from 2004 to 2014 and nine metrological, geographical, and anthropogenic explanatory factors. Urban flood susceptibility is mapped and compared using logistic regression, artificial neural networks, and a support vector machine. Model performances are evaluated using four evaluation indices (accuracy, precision, recall, and F-score) as well as the receiver operating characteristic curve. The results show that WELLSVM can better utilize the spatial information (unlabeled data), and it outperforms all comparison models. The high-quality WELLSVM flood susceptibility map is thus applicable to efficient urban flood management.

KW - Beijing

KW - Flood susceptibility

KW - Semi-supervised machine learning model

KW - Urban area

KW - Weakly labeled support vector machine

UR - http://www.scopus.com/inward/record.url?scp=85059465466&partnerID=8YFLogxK

U2 - 10.1016/j.scitotenv.2018.12.217

DO - 10.1016/j.scitotenv.2018.12.217

M3 - Article

VL - 659

SP - 940

EP - 949

JO - Science of The Total Environment

T2 - Science of The Total Environment

JF - Science of The Total Environment

SN - 0048-9697

ER -