.png){kind=icon}
返回
A rapid and efficient method for flash flood simulation based on deep learning
.png){kind=icon}
OA
AI
DOI：10.1080/19942060.2024.2407016.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Among the various natural disasters, the death caused by flash flood is the highest. Recently, the combination of deep learning methods and hydrodynamic models has shown superior performance in the simulation of urban and plain areas. However, when dealing with flash flood simulation, the research still faces numerous challenges due to limitations such as data scarcity, small sample sizes, complex terrain, and high levels of uncertainty. Therefore, in this study, we innovatively combined deep learning methods with flash flood simulation and proposed a TCN model to predict the spatiotemporal dynamics of flash floods. First, we extracted the typical rainfall patterns in the study area and used design storm methods to generate a hydrograph dataset, which includes various rainfall patterns and return periods. Then, we developed a Temporal Convolutional Network (TCN) model to predict flash floods. Finally, the benchmark test was carried out by Convolutional Neural Network (CNN), which further proved the performance of TCN. The study found that: (1) The TCN model effectively predicts flash floods, with average MAE, RMSE and NSE reaching 0.04, 0.17 and 0.834 on the validation set. However, the CNN model performed better in small flood scenarios; (2) Error boxplots show that simulation errors for both models increase with the flood volume, and reach the maximum around the flood peak, but the TCN model demonstrated better stability and fewer outliers; (3) For the change of water depth at key points, both TCN and CNN effectively capture the fluctuation of water depth with time in the early stage of flood, but TCN showed higher consistency in the recession period. The results show that the rapid simulation method of flash flood based on TCN can better capture the dynamic characteristics of flash flood, and has been well applied in mountainous areas, which provides a new method for the prediction and early warning of flash flood disasters.
Keyword:
Flash flood simulation
deep learning
surrogate model
rapid flood modelling
期刊
IF:
5.4
论文数:
1.4K
被引数:
3.9K
机构
暂无机构信息
引用论文
Deep learning for natural language processing: advantages and challenges自然语言处理的深度学习: 优势与挑战.png)
NATIONAL SCIENCE REVIEW
OAAI
NATIONAL SCIENCE REVIEWIF17.1
OAAI分享收藏Numerical investigation of flash flood dynamics due to cascading failures of natural landslide damsENGINEERING GEOLOGY
ENGINEERING GEOLOGYIF8.4
PREAI
分享收藏Assessment of Flood Inundation by Coupled 1D/2D Hydrodynamic Modeling: A Case Study in Mountainous Watersheds along the Coast of Southeast ChinaWATEROAAI
WATERIF3
OAAI分享收藏分享收藏
查看更多内容