Articles | Volume 21, issue 4
https://doi.org/10.5194/nhess-21-1179-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-1179-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Apr 2021
Research article |
| 06 Apr 2021
| 06 Apr 2021
Online urban-waterlogging monitoring based on a recurrent neural network for classification of microblogging text
Hui Liu
Business School, Changzhou University, Changzhou, 213159, China
Business School, Changzhou University, Changzhou, 213159, China
Wenhao Zhang
Business School, Changzhou University, Changzhou, 213159, China
Hanyue Zhang
Business School, Changzhou University, Changzhou, 213159, China
Fei Gao
Business School, Changzhou University, Changzhou, 213159, China
Jinping Tong
CORRESPONDING AUTHOR
Business School, Changzhou University, Changzhou, 213159, China
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Cited
14 citations as recorded by crossref.
- Automatic Generation of the Draft Procuratorial Suggestions Based on an Extractive Summarization Method: BERTSLCA Y. Sun et al. 10.1155/2021/3591894
- Flood impacts on urban road connectivity in southern China R. Zhou et al. 10.1038/s41598-022-20882-5
- Research on urban waterlogging risk prediction based on the coupling of the BP neural network and SWMM model J. Zhang et al. 10.2166/wcc.2023.076
- Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review H. Sun et al. 10.3390/rs16020350
- Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique H. Huang et al. 10.1016/j.ecoinf.2024.102904
- Spatiotemporal assessment of urban flooding hazard using social media: A case study of Zhengzhou ‘7·20’ J. Peng & J. Zhang 10.1016/j.envsoft.2024.106021
- Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives Y. Himeur et al. 10.1016/j.inffus.2022.06.003
- Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis A. Safaei-Moghadam et al. 10.5194/nhess-23-1-2023
- Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks X. Hu et al. 10.1155/2024/9601590
- Study on Urban Expansion and Population Density Changes Based on the Inverse S-Shaped Function H. Lu et al. 10.3390/su151310464
- A hybrid connectionist enhanced oil recovery model with real-time probabilistic risk assessment M. Shah et al. 10.1016/j.geoen.2023.211760
- The quantitative assessment of impact of pumping capacity and LID on urban flood susceptibility based on machine learning Y. Wu et al. 10.1016/j.jhydrol.2023.129116
- Coupling machine learning and weather forecast to predict farmland flood disaster: A case study in Yangtze River basin Z. Jiang et al. 10.1016/j.envsoft.2022.105436
- Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm P. Zhang et al. 10.3390/math12081127
14 citations as recorded by crossref.
- Automatic Generation of the Draft Procuratorial Suggestions Based on an Extractive Summarization Method: BERTSLCA Y. Sun et al. 10.1155/2021/3591894
- Flood impacts on urban road connectivity in southern China R. Zhou et al. 10.1038/s41598-022-20882-5
- Research on urban waterlogging risk prediction based on the coupling of the BP neural network and SWMM model J. Zhang et al. 10.2166/wcc.2023.076
- Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review H. Sun et al. 10.3390/rs16020350
- Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique H. Huang et al. 10.1016/j.ecoinf.2024.102904
- Spatiotemporal assessment of urban flooding hazard using social media: A case study of Zhengzhou ‘7·20’ J. Peng & J. Zhang 10.1016/j.envsoft.2024.106021
- Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives Y. Himeur et al. 10.1016/j.inffus.2022.06.003
- Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis A. Safaei-Moghadam et al. 10.5194/nhess-23-1-2023
- Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks X. Hu et al. 10.1155/2024/9601590
- Study on Urban Expansion and Population Density Changes Based on the Inverse S-Shaped Function H. Lu et al. 10.3390/su151310464
- A hybrid connectionist enhanced oil recovery model with real-time probabilistic risk assessment M. Shah et al. 10.1016/j.geoen.2023.211760
- The quantitative assessment of impact of pumping capacity and LID on urban flood susceptibility based on machine learning Y. Wu et al. 10.1016/j.jhydrol.2023.129116
- Coupling machine learning and weather forecast to predict farmland flood disaster: A case study in Yangtze River basin Z. Jiang et al. 10.1016/j.envsoft.2022.105436
- Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm P. Zhang et al. 10.3390/math12081127
Latest update: 20 Jan 2025
Short summary
We trained a recurrent neural network model to classify microblogging posts related to urban waterlogging and establish an online monitoring system of urban waterlogging caused by flood disasters. We manually curated more than 4400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely determine urban waterlogging.
We trained a recurrent neural network model to classify microblogging posts related to urban...
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