A spatial-temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features.
A spatial-temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features.
复制标题
DOI：
10.1038/s41598-023-32548-x
复制
发表时间：
2023-04-25
影响因子：
4.6
通讯作者：
Mostafavi, Ali
中科院分区：
文献类型：
--
作者：
Farahmand, Hamed;Xu, Yuanchang;Mostafavi, Ali
洪水临近预报是指在极端天气事件发生时对洪水状况进行近期预测，以提高对情况的认识。本研究的目的是通过整合基于物理和人类感知的特征，采用并测试一种新的结构化深度学习模型用于城市洪水临近预报。我们提出了一个新的计算建模框架，包括基于注意力的时空图卷积网络（ASTGCN）模型和不同的数据流，这些数据流是实时收集的，预处理的，并输入到模型中，以考虑空间和时间信息以及改善洪水临近预报的依赖关系。计算建模框架的新奇是三方面的：首先，由于空间和时间图卷积模块，该模型能够考虑洪水传播中的空间和时间依赖性;其次，它能够捕获可以信号洪水状态的异构时间数据流的影响，包括基于物理的特征（例如，降雨强度和水位）和人类感测的数据（例如，居民的水浸报告和人类活动的波动）对洪水临近预报的影响。第三，它的注意力机制，使模型能够将其重点放在最有影响力的功能，动态变化，影响洪水临近预报。我们展示了建模框架在德克萨斯州哈里斯县作为研究区域和2017年飓风哈维作为洪水事件的背景下的应用。三类特征用于临近预报不同普查区的洪水淹没程度：（i）捕获不同位置的空间特征并影响其洪水状态相似性的静态特征，（ii）捕获水动力变量变化的基于物理学的动态特征，及（iii）异质人类-所感测的动态特征捕捉居民活动的各个方面，其可以提供关于洪水状态的信息。结果表明，ASTGCN模型在城市洪水淹没临近预报中具有上级性能，准确率为0.808，召回率为0.891，优于其他模型。此外，ASTGCN模型的性能提高时，异构的动态功能添加到模型中，只依赖于基于物理的功能，这表明使用异构的人类感知数据的洪水临近预报的承诺。考虑到模型的比较结果，建议的建模框架有可能被更多的调查时，更多的历史事件的数据，以开发一个预测工具，提供社区响应者在城市洪水洪水淹没的增强预测。
Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial–temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold: first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features (e.g., rainfall intensity and water elevation) and human-sensed data (e.g., residents’ flood reports and fluctuations of human activity) on flood nowcasting. Third, its attention mechanism enables the model to direct its focus to the most influential features that vary dynamically and influence the flood nowcasting. We show the application of the modeling framework in the context of Harris County, Texas, as the study area and 2017 Hurricane Harvey as the flood event. Three categories of features are used for nowcasting the extent of flood inundation in different census tracts: (i) static features that capture spatial characteristics of various locations and influence their flood status similarity, (ii) physics-based dynamic features that capture changes in hydrodynamic variables, and (iii) heterogeneous human-sensed dynamic features that capture various aspects of residents’ activities that can provide information regarding flood status. Results indicate that the ASTGCN model provides superior performance for nowcasting of urban flood inundation at the census-tract level, with precision 0.808 and recall 0.891, which shows the model performs better compared with other state-of-the-art models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting. Given the results of the comparisons of the models, the proposed modeling framework has the potential to be more investigated when more data of historical events are available in order to develop a predictive tool to provide community responders with an enhanced prediction of the flood inundation during urban flood.
登录
查看更多内容
影响因子：
9.8
作者：
Hosseini, Farzaneh Sajedi;Choubin, Bahram;Haghighi, Ali Torabi
通讯作者：
Haghighi, Ali Torabi
影响因子：
3.4
作者：
Brivio, PA;Colombo, R;Tomasoni, R
通讯作者：
Tomasoni, R
影响因子：
9.6
作者：
Chen, Sikai;Leng, Yue;Labi, Samuel
通讯作者：
Labi, Samuel
影响因子：
4.6
作者：
Apel, H;Thieken, AH;Blöschl, G
通讯作者：
Blöschl, G
DOI：
10.1007/s40010-017-0461-7
发表时间：
2017-12-01
影响因子：
0.9
作者：
Amarnath, Giriraj;Matheswaran, Karthikeyan;Yoshimoto, Shuhei
通讯作者：
Yoshimoto, Shuhei