Data-Driven Boundary Correction and Optimization of a Nearshore Wave and Hydrodynamic Model to Enable Rapid Environmental Assessment

2011 ◽  
Author(s):  
James M. Kaihatu
Keyword(s):  
Environmental Assessment ◽  
Hydrodynamic Model ◽  
Data Driven ◽  
Boundary Correction

scholarly journals A deep learning technique-based data-driven model for accurate and rapid flood prediction

2022 ◽  
Author(s):  
Qianqian Zhou ◽  
Shuai Teng ◽  
Xiaoting Liao ◽  
Zuxiang Situ ◽  
Junman Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Hydrodynamic Model ◽  
Bayesian Optimization ◽  
Data Driven ◽  
Real Time Control ◽  
Flood Prediction ◽  
Urban Flood ◽  
Learning Technique ◽  
Flood Maps

Abstract. An accurate and rapid urban flood prediction model is essential to support decision-making on flood management, especially under increasing extreme precipitation conditions driven by climate change and urbanization. This study developed a deep learning technique-based data-driven flood prediction model based on an integration of LSTM network and Bayesian optimization. A case study in north China was applied to test the model performance and the results clearly showed that the model can accurately predict flood maps for various hyetograph inputs, meanwhile with substantial improvements in computation time. The model predicted flood maps 19,585 times faster than the physical-based hydrodynamic model and achieved a mean relative error of 9.5 %. For retrieving the spatial patterns of water depths, the degree of similarity of the flood maps was very high. In a best case, the difference between the ground truth and model prediction was only 0.76 % and the spatial distributions of inundated paths and areas were almost identical. The proposed model showed a robust generalizability and high computational efficiency, and can potentially replace and/or complement the conventional hydrodynamic model for urban flood assessment and management, particularly in applications of real time control, optimization and emergency design and plan.

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