Articles | Volume 27, issue 9
https://doi.org/10.5194/hess-27-1791-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-1791-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 May 2023
Research article |
| 05 May 2023
| 05 May 2023
A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions
Qianqian Zhou
CORRESPONDING AUTHOR
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Shuai Teng
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Zuxiang Situ
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Xiaoting Liao
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Junman Feng
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Gongfa Chen
CORRESPONDING AUTHOR
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Jianliang Zhang
Guangdong Communication Planning and Design Institute Group Co., Ltd, Guangzhou 510507, China
Zonglei Lu
GRUNDFOS Pumps (Shanghai) Co., Ltd. Guangzhou Branch, Guangzhou 510095, China
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Cited
14 citations as recorded by crossref.
- Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects D. Dalla Torre et al. 10.1007/s42452-024-05819-z
- Assessing the environmental impacts of flooding in Brazil using the flood area segmentation network deep learning model A. Şener & B. Ergen 10.1007/s11069-024-06914-5
- Improving urban flood prediction using LSTM-DeepLabv3+ and Bayesian optimization with spatiotemporal feature fusion Z. Situ et al. 10.1016/j.jhydrol.2024.130743
- River flood prediction through flow level modeling using multi-attention encoder-decoder-based TCN with filter-wrapper feature selection G. Selva Jeba & P. Chitra 10.1007/s12145-024-01446-9
- Enhancing flood risk assessment in urban areas by integrating hydrodynamic models and machine learning techniques A. Khoshkonesh et al. 10.1016/j.scitotenv.2024.175859
- Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning F. Ghobadi et al. 10.1016/j.jhydrol.2024.130772
- Rapid urban flood inundation forecasting using a physics-informed deep learning approach F. Yang et al. 10.1016/j.jhydrol.2024.131998
- Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends A. Saha & S. Chandra Pal 10.1016/j.jhydrol.2024.130907
- A hybrid rainfall-runoff model: integrating initial loss and LSTM for improved forecasting W. Wang et al. 10.3389/fenvs.2023.1261239
- The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management V. Kumar et al. 10.3390/su151310543
- Performance benchmarking on several regression models applied in urban flash flood risk assessment H. Hu et al. 10.1007/s11069-023-06341-y
- A novel strategy for flood flow Prediction: Integrating Spatio-Temporal information through a Two-Dimensional hidden layer structure Y. Wang et al. 10.1016/j.jhydrol.2024.131482
- Substantial Enhancement of Overall Efficiency and Effectiveness of the Pasteurization and Packaging Process Using Artificial Intelligence in the Food Industry P. Singh et al. 10.1007/s11947-024-03527-5
- Enhancing Flood Susceptibility Modeling: a Hybrid Deep Neural Network with Statistical Learning Algorithms for Predicting Flood Prone Areas M. Ghobadi & M. Ahmadipari 10.1007/s11269-024-03770-7
13 citations as recorded by crossref.
- Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects D. Dalla Torre et al. 10.1007/s42452-024-05819-z
- Assessing the environmental impacts of flooding in Brazil using the flood area segmentation network deep learning model A. Şener & B. Ergen 10.1007/s11069-024-06914-5
- Improving urban flood prediction using LSTM-DeepLabv3+ and Bayesian optimization with spatiotemporal feature fusion Z. Situ et al. 10.1016/j.jhydrol.2024.130743
- River flood prediction through flow level modeling using multi-attention encoder-decoder-based TCN with filter-wrapper feature selection G. Selva Jeba & P. Chitra 10.1007/s12145-024-01446-9
- Enhancing flood risk assessment in urban areas by integrating hydrodynamic models and machine learning techniques A. Khoshkonesh et al. 10.1016/j.scitotenv.2024.175859
- Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning F. Ghobadi et al. 10.1016/j.jhydrol.2024.130772
- Rapid urban flood inundation forecasting using a physics-informed deep learning approach F. Yang et al. 10.1016/j.jhydrol.2024.131998
- Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends A. Saha & S. Chandra Pal 10.1016/j.jhydrol.2024.130907
- A hybrid rainfall-runoff model: integrating initial loss and LSTM for improved forecasting W. Wang et al. 10.3389/fenvs.2023.1261239
- The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management V. Kumar et al. 10.3390/su151310543
- Performance benchmarking on several regression models applied in urban flash flood risk assessment H. Hu et al. 10.1007/s11069-023-06341-y
- A novel strategy for flood flow Prediction: Integrating Spatio-Temporal information through a Two-Dimensional hidden layer structure Y. Wang et al. 10.1016/j.jhydrol.2024.131482
- Substantial Enhancement of Overall Efficiency and Effectiveness of the Pasteurization and Packaging Process Using Artificial Intelligence in the Food Industry P. Singh et al. 10.1007/s11947-024-03527-5
Latest update: 20 Nov 2024
Short summary
A deep-learning-based data-driven model for flood predictions in temporal and spatial dimensions, with the integration of a long short-term memory network, Bayesian optimization, and transfer learning is proposed. The model accurately predicts water depths and flood time series/dynamics for hyetograph inputs, with substantial improvements in computational time. With transfer learning, the model was well applied to a new case study and showed robust compatibility and generalization ability.
A deep-learning-based data-driven model for flood predictions in temporal and spatial...
