Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing
Accurate and timely flood forecasting, facilitated by remote sensing technology, is crucial to mitigate the damage and loss of life caused by floods. However, despite years of research, accurate flood prediction still faces numerous challenges, including complex spatiotemporal features and varied fl...
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IEEE
2024-01-01
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Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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Online Access: | https://ieeexplore.ieee.org/document/10380452/ |
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author | Jiange Jiang Chen Chen Yang Zhou Stefano Berretti Lei Liu Qingqi Pei Jianming Zhou Shaohua Wan |
author_facet | Jiange Jiang Chen Chen Yang Zhou Stefano Berretti Lei Liu Qingqi Pei Jianming Zhou Shaohua Wan |
author_sort | Jiange Jiang |
collection | DOAJ |
description | Accurate and timely flood forecasting, facilitated by remote sensing technology, is crucial to mitigate the damage and loss of life caused by floods. However, despite years of research, accurate flood prediction still faces numerous challenges, including complex spatiotemporal features and varied flood patterns influenced by multiple variables. Moreover, long-term flood forecasting is always tricky due to the constantly changing conditions of the surrounding environment. In this study, we propose a heterogeneous dynamic temporal graph convolutional network (HD-TGCN) for flood forecasting. Specifically, we designed a dynamic temporal graph convolution module (D-TGCM) to generate a dynamic adjacency matrix by incorporating a multihead self-attention mechanism, enabling our model to capture the dynamic spatiotemporal features of flood data by utilizing temporal graph convolution operations on the dynamic matrix. Furthermore, to reflect the impact of multiple meteorological and hydrological features on the heterogeneity of flood data, we propose a novel approach that utilizes multiple parallel D-TGCMs for processing heterogeneous graph data and implements a fusion mechanism to capture varied flood patterns influenced by multiple variables. Experiments conducted on a real dataset in Wuyuan County, Jiangxi Province, demonstrate that the HD-TGCN outperforms the state-of-the-art flood prediction models in mean absolute error, Nash–Sutcliffe efficiency, and root-mean-square error, with improvements of 80.32%, 0.15%, and 73.99%, respectively, providing a more accurate flood forecasting method that will play a critical role in future flood disaster prevention and control. |
first_indexed | 2024-03-08T12:10:36Z |
format | Article |
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institution | Directory Open Access Journal |
issn | 2151-1535 |
language | English |
last_indexed | 2024-03-08T12:10:36Z |
publishDate | 2024-01-01 |
publisher | IEEE |
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series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
spelling | doaj.art-5cb13d757cfc449f9bb32f4a0206bd002024-01-23T00:00:22ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352024-01-01173108312210.1109/JSTARS.2023.334916210380452Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote SensingJiange Jiang0https://orcid.org/0000-0003-2644-3224Chen Chen1https://orcid.org/0000-0002-4971-5029Yang Zhou2https://orcid.org/0009-0001-6603-3580Stefano Berretti3https://orcid.org/0000-0003-1219-4386Lei Liu4https://orcid.org/0000-0001-8173-0408Qingqi Pei5https://orcid.org/0000-0001-7601-5434Jianming Zhou6https://orcid.org/0000-0002-4971-5029Shaohua Wan7https://orcid.org/0000-0001-7013-9081School of Telecommunications Engineering, Xidian University, Xi'an, ChinaSchool of Telecommunications Engineering, Xidian University, Xi'an, ChinaMinistry of Water Resources of China, Beijing, ChinaDepartment of Information Engineering, University of Florence, Florence, ItalySchool of Telecommunications Engineering, Xidian University, Xi'an, ChinaSchool of Telecommunications Engineering, Xidian University, Xi'an, ChinaChina Unicom Shenzhen Branch, Shenzhen, ChinaShenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, ChinaAccurate and timely flood forecasting, facilitated by remote sensing technology, is crucial to mitigate the damage and loss of life caused by floods. However, despite years of research, accurate flood prediction still faces numerous challenges, including complex spatiotemporal features and varied flood patterns influenced by multiple variables. Moreover, long-term flood forecasting is always tricky due to the constantly changing conditions of the surrounding environment. In this study, we propose a heterogeneous dynamic temporal graph convolutional network (HD-TGCN) for flood forecasting. Specifically, we designed a dynamic temporal graph convolution module (D-TGCM) to generate a dynamic adjacency matrix by incorporating a multihead self-attention mechanism, enabling our model to capture the dynamic spatiotemporal features of flood data by utilizing temporal graph convolution operations on the dynamic matrix. Furthermore, to reflect the impact of multiple meteorological and hydrological features on the heterogeneity of flood data, we propose a novel approach that utilizes multiple parallel D-TGCMs for processing heterogeneous graph data and implements a fusion mechanism to capture varied flood patterns influenced by multiple variables. Experiments conducted on a real dataset in Wuyuan County, Jiangxi Province, demonstrate that the HD-TGCN outperforms the state-of-the-art flood prediction models in mean absolute error, Nash–Sutcliffe efficiency, and root-mean-square error, with improvements of 80.32%, 0.15%, and 73.99%, respectively, providing a more accurate flood forecasting method that will play a critical role in future flood disaster prevention and control.https://ieeexplore.ieee.org/document/10380452/Deep learningdynamic graph convolutionflood forecastingmultivariable predictionspatiotemporal graph data |
spellingShingle | Jiange Jiang Chen Chen Yang Zhou Stefano Berretti Lei Liu Qingqi Pei Jianming Zhou Shaohua Wan Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning dynamic graph convolution flood forecasting multivariable prediction spatiotemporal graph data |
title | Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing |
title_full | Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing |
title_fullStr | Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing |
title_full_unstemmed | Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing |
title_short | Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing |
title_sort | heterogeneous dynamic graph convolutional networks for enhanced spatiotemporal flood forecasting by remote sensing |
topic | Deep learning dynamic graph convolution flood forecasting multivariable prediction spatiotemporal graph data |
url | https://ieeexplore.ieee.org/document/10380452/ |
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