A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model

Yaoxing Liao; Zhaoli Wang; Chengguang Lai; Chong-Yu Xu

doi:10.1007/s13753-023-00481-2

Volume 14 Issue 2

Apr. 2023

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Article Navigation > International Journal of Disaster Risk Science > 2023 > 14(2): 253-268

Yaoxing Liao, Zhaoli Wang, Chengguang Lai, Chong-Yu Xu. A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model[J]. International Journal of Disaster Risk Science, 2023, 14(2): 253-268. doi: 10.1007/s13753-023-00481-2

Citation:

Yaoxing Liao, Zhaoli Wang, Chengguang Lai, Chong-Yu Xu. A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model[J]. International Journal of Disaster Risk Science, 2023, 14(2): 253-268. doi: 10.1007/s13753-023-00481-2

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A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model

doi: 10.1007/s13753-023-00481-2

1. State Key Laboratory of Subtropical Building Science, School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510641, China;
2. Pazhou Lab, Guangzhou, 510335, China;
3. Department of Geosciences, University of Oslo, 0316, Oslo, Norway

Funds:

This research acquired financial or data support of the National Key R&D Program of China (2021YFC3001000), the National Natural Science Foundation of China (U1911204, 51879107), the Natural Science Foundation of Guangdong Province (2023B1515020087, 2022A1515010019), and the Fund of Science and Technology Program of Guangzhou (202102020216).

Accepted Date: 2023-03-13

Available Online: 2023-04-28

Publish Date: 2023-04-11

Abstract

Abstract

Fast and accurate prediction of urban flood is of considerable practical importance to mitigate the effects of frequent flood disasters in advance. To improve urban flood prediction efficiency and accuracy, we proposed a framework for fast mapping of urban flood:a coupled model based on physical mechanisms was first constructed, a rainfall-inundation database was generated, and a hybrid flood mapping model was finally proposed using the multi-objective random forest (MORF) method. The results show that the coupled model had good reliability in modelling urban flood, and 48 rainfall-inundation scenarios were then specified. The proposed hybrid MORF model in the framework also demonstrated good performance in predicting inundated depth under the observed and scenario rainfall events. The spatial inundated depths predicted by the MORF model were close to those of the coupled model, with differences typically less than 0.1 m and an average correlation coefficient reaching 0.951. The MORF model, however, achieved a computational speed of 200 times faster than the coupled model. The overall prediction performance of the MORF model was also better than that of the k-nearest neighbor model. Our research provides a novel approach to rapid urban flood mapping and flood early warning.
- Coupled model,
- Designed rainfall events,
- Multi-objective random forest (MORF) method,
- Rainfall-inundation database,
- Urban flood prediction

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References(61)

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