Drainage Pattern Recognition Method Using Graph Convolutional Networks Combined With Three‐Dimensional Elevation Features

ABSTRACT Drainage pattern recognition plays a crucial role in flood management, hydraulic engineering site selection, and biodiversity maintenance. Although deep learning methods have been widely employed in drainage pattern recognition in recent years, most research has focused on two‐dimensional f...

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Vydané v:	Transactions in GIS Ročník 29; číslo 1
Hlavní autori:	Qiang, Bo, Liu, Tao, Du, Ping, Li, Pengpeng, Wang, Wenning, Xu, Shenglu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Blackwell Publishing Ltd 01.02.2025
Predmet:	Accuracy Artificial neural networks Biodiversity Chebyshev approximation Deep learning Drainage Drainage patterns Flood control Flood management Geographic information systems graph convolutional network Hydraulic engineering Machine learning Mathematical morphology Pattern recognition Polynomials river Site selection Support vector machines three‐dimensional elevation
ISSN:	1361-1682, 1467-9671
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Abstract	ABSTRACT Drainage pattern recognition plays a crucial role in flood management, hydraulic engineering site selection, and biodiversity maintenance. Although deep learning methods have been widely employed in drainage pattern recognition in recent years, most research has focused on two‐dimensional features, with limited attention given to the impact of three‐dimensional elevation features. This study, therefore, introduces a graph convolutional network (GCN) approach that incorporates three‐dimensional elevation features. This method integrates three‐dimensional elevation features into the feature system of deep learning‐based drainage pattern recognition for the first time, combining two‐dimensional geometric and topological features to comprehensively capture variations in both vertical and planar morphology of drainage patterns. Specifically, this study extracts representative drainage pattern samples (e.g., dendritic, skeleton, parallel, and fan) from OSM and HydroSHEDS river vector data. Six features are designed based on the micro‐to‐macro processes involved in drainage pattern development, and a novel drainage pattern feature description index is proposed. A GCN model architecture based on the first‐order Chebyshev polynomial is then developed to extract both global and local information of drainage patterns under three‐dimensional elevation features. Experimental results demonstrate that the recognition accuracy of this method achieves 90% on the test dataset, with precision, recall, and F1 scores improving by at least 3% compared to methods such as support vector machines (SVM) and GraphSAGE. This suggests that the feature description index incorporating three‐dimensional elevation features more comprehensively reflects the development process of drainage patterns, thereby improving recognition accuracy and offering new methods and perspectives for drainage pattern research.
AbstractList	Drainage pattern recognition plays a crucial role in flood management, hydraulic engineering site selection, and biodiversity maintenance. Although deep learning methods have been widely employed in drainage pattern recognition in recent years, most research has focused on two‐dimensional features, with limited attention given to the impact of three‐dimensional elevation features. This study, therefore, introduces a graph convolutional network (GCN) approach that incorporates three‐dimensional elevation features. This method integrates three‐dimensional elevation features into the feature system of deep learning‐based drainage pattern recognition for the first time, combining two‐dimensional geometric and topological features to comprehensively capture variations in both vertical and planar morphology of drainage patterns. Specifically, this study extracts representative drainage pattern samples (e.g., dendritic, skeleton, parallel, and fan) from OSM and HydroSHEDS river vector data. Six features are designed based on the micro‐to‐macro processes involved in drainage pattern development, and a novel drainage pattern feature description index is proposed. A GCN model architecture based on the first‐order Chebyshev polynomial is then developed to extract both global and local information of drainage patterns under three‐dimensional elevation features. Experimental results demonstrate that the recognition accuracy of this method achieves 90% on the test dataset, with precision, recall, and F1 scores improving by at least 3% compared to methods such as support vector machines (SVM) and GraphSAGE. This suggests that the feature description index incorporating three‐dimensional elevation features more comprehensively reflects the development process of drainage patterns, thereby improving recognition accuracy and offering new methods and perspectives for drainage pattern research. ABSTRACT Drainage pattern recognition plays a crucial role in flood management, hydraulic engineering site selection, and biodiversity maintenance. Although deep learning methods have been widely employed in drainage pattern recognition in recent years, most research has focused on two‐dimensional features, with limited attention given to the impact of three‐dimensional elevation features. This study, therefore, introduces a graph convolutional network (GCN) approach that incorporates three‐dimensional elevation features. This method integrates three‐dimensional elevation features into the feature system of deep learning‐based drainage pattern recognition for the first time, combining two‐dimensional geometric and topological features to comprehensively capture variations in both vertical and planar morphology of drainage patterns. Specifically, this study extracts representative drainage pattern samples (e.g., dendritic, skeleton, parallel, and fan) from OSM and HydroSHEDS river vector data. Six features are designed based on the micro‐to‐macro processes involved in drainage pattern development, and a novel drainage pattern feature description index is proposed. A GCN model architecture based on the first‐order Chebyshev polynomial is then developed to extract both global and local information of drainage patterns under three‐dimensional elevation features. Experimental results demonstrate that the recognition accuracy of this method achieves 90% on the test dataset, with precision, recall, and F1 scores improving by at least 3% compared to methods such as support vector machines (SVM) and GraphSAGE. This suggests that the feature description index incorporating three‐dimensional elevation features more comprehensively reflects the development process of drainage patterns, thereby improving recognition accuracy and offering new methods and perspectives for drainage pattern research.
Author	Du, Ping Liu, Tao Li, Pengpeng Qiang, Bo Wang, Wenning Xu, Shenglu
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Notes	Funding This work was supported by National Natural Science Foundation of China: 42261076, Key Research and Development Project of Lanzhou JiaoTong University: LZJTU‐ZDYF2301, Major Technology Project of Gansu Province: 22ZD6GA010 and Gansu Youth Science and Technology Fund: 24JRRA275. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
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References	2009; 45 2022; 233 2019; 95 2019; 6 2013; 27 2023; 4 2010; 19 2023; 59 2023; 16 2000; 63 1988; 54 2011; 30 2020; 16 2007 2004; 6 2024; 53 2012; XXXIX‐B2 2024; 39 2001; 40 2011; 132 1987; 15 2016; 4 2017; 31 2021; 11 2020; 1 2024; 8 2023; 27 2023; 211 1967; 51 2019; 47 2003; 26 2022; 36 2017 2016 2020; 354 2020; 45 2008; 113 2013 2001; 438 2024; 27 2019; 150 2022; 107 1994; 30 1998; 34 2019; 572 2007; 27 e_1_2_10_23_1 e_1_2_10_46_1 e_1_2_10_24_1 e_1_2_10_45_1 e_1_2_10_21_1 e_1_2_10_44_1 e_1_2_10_22_1 e_1_2_10_43_1 e_1_2_10_42_1 e_1_2_10_20_1 e_1_2_10_41_1 e_1_2_10_40_1 e_1_2_10_2_1 e_1_2_10_4_1 e_1_2_10_18_1 e_1_2_10_3_1 e_1_2_10_19_1 e_1_2_10_6_1 e_1_2_10_16_1 e_1_2_10_39_1 e_1_2_10_5_1 e_1_2_10_17_1 e_1_2_10_38_1 e_1_2_10_8_1 e_1_2_10_14_1 e_1_2_10_37_1 e_1_2_10_7_1 e_1_2_10_15_1 e_1_2_10_36_1 e_1_2_10_12_1 e_1_2_10_35_1 e_1_2_10_9_1 e_1_2_10_13_1 e_1_2_10_34_1 e_1_2_10_10_1 e_1_2_10_33_1 e_1_2_10_11_1 e_1_2_10_32_1 e_1_2_10_31_1 e_1_2_10_30_1 e_1_2_10_29_1 e_1_2_10_27_1 e_1_2_10_28_1 e_1_2_10_49_1 e_1_2_10_25_1 e_1_2_10_48_1 e_1_2_10_26_1 e_1_2_10_47_1
References_xml	– volume: 34 start-page: 1809 issue: 7 year: 1998 end-page: 1818 article-title: Energy Dissipation Theories and Optimal Channel Characteristics of River Networks publication-title: Water Resources Research – volume: 95 start-page: 308 year: 2019 end-page: 318 article-title: Learning Graph Structure via Graph Convolutional Networks publication-title: Pattern Recognition – volume: 150 start-page: 259 year: 2019 end-page: 273 article-title: A Graph Convolutional Neural Network for Classification of Building Patterns Using Spatial Vector Data publication-title: ISPRS Journal of Photogrammetry and Remote Sensing – volume: 15 start-page: 813 issue: 9 year: 1987 end-page: 816 article-title: Effect of Regional Slope on Drainage Networks publication-title: Geology – volume: XXXIX‐B2 start-page: 29 year: 2012 end-page: 34 article-title: A Study of Variables Characterizing Drainage Patterns in River Networks publication-title: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences – volume: 30 start-page: 161 issue: 2 year: 1994 end-page: 174 article-title: A Numerical Approach to the Analysis and Classification of Channel Network Patterns publication-title: Water Resources Research – volume: 27 start-page: 293 issue: 4 year: 2007 end-page: 309 article-title: Place Experience, Gestalt, and the Human–Nature Relationship publication-title: Journal of Environmental Psychology – volume: 27 start-page: 752 issue: 3 year: 2023 end-page: 776 article-title: Identification of Drainage Patterns Using a Graph Convolutional Neural Network publication-title: Transactions in GIS – year: 2007 – volume: 19 start-page: 27 issue: 1 year: 2010 end-page: 39 article-title: Land Use and Climatic Factors Structure Regional Patterns in Soil Microbial Communities publication-title: Global Ecology and Biogeography – volume: 354 year: 2020 article-title: Deep Learning‐Based Approach for Landform Classification From Integrated Data Sources of Digital Elevation Model and Imagery publication-title: Geomorphology – volume: 59 start-page: e2022W issue: 7 year: 2023 end-page: e33681W article-title: Hydrological Object‐Based Flow Direction Model for Constructing a Lake‐Stream Topological System publication-title: Water Resources Research – volume: 233 year: 2022 article-title: Geomorphometry and Terrain Analysis: Data, Methods, Platforms and Applications publication-title: Earth‐Science Reviews – volume: 31 start-page: 387 issue: 2 year: 2017 end-page: 404 article-title: A Peak‐Cluster Assessment Method for the Identification of Upland Planation Surfaces publication-title: International Journal of Geographical Information Science – volume: 438 start-page: 183 issue: 13 year: 2001 end-page: 211 article-title: Downstream and Upstream Influence in River Meandering. Part 1. General Theory and Application to Overdeepening publication-title: Journal of Fluid Mechanics – volume: 40 start-page: 37 issue: 1–2 year: 2001 end-page: 55 article-title: Impacts of Surface Elevation on the Growth and Scaling Properties of Simulated River Networks publication-title: Geomorphology – volume: 211 year: 2023 article-title: Automatic Segmentation of Parallel Drainage Patterns Supported by a Graph Convolution Neural Network publication-title: Expert Systems With Applications – year: 2016 – volume: 4 start-page: 108 issue: 2 year: 2016 end-page: 120 article-title: Identification of Suitable Sites for Rainwater Harvesting Structures in Arid and Semi‐Arid Regions: A Review publication-title: International Soil and Water Conservation Research – volume: 4 start-page: 4 issue: 1 year: 2023 article-title: Past Rainfall‐Driven Erosion on the Chinese Loess Plateau Inferred From Archaeological Evidence From Wucheng City, Shanxi publication-title: Communications Earth & Environment – volume: 16 start-page: 593 issue: 1 year: 2023 end-page: 619 article-title: Drainage Pattern Recognition Method Considering Local Basin Shape Based on Graph Neural Network publication-title: International Journal of Digital Earth – volume: 107 year: 2022 article-title: A Recognition Method for Drainage Patterns Using a Graph Convolutional Network publication-title: International Journal of Applied Earth Observation and Geoinformation – volume: 27 start-page: 1622 issue: 5 year: 2024 end-page: 1637 article-title: Classification of Urban Interchange Patterns Using a Model Combining Shape Context Descriptor and Graph Convolutional Neural Network publication-title: Geo‐Spatial Information Science – volume: 113 issue: F2 year: 2008 article-title: Identification and Characterization of Dendritic, Parallel, Pinnate, Rectangular, and Trellis Networks Based on Deviations From Planform Self‐Similarity publication-title: Journal of Geophysical Research: Earth Surface – volume: 47 start-page: 263 issue: 3 year: 2019 end-page: 266 article-title: High Curvatures Drive River Meandering publication-title: Geology – volume: 26 start-page: 295 issue: 3 year: 2003 end-page: 309 article-title: The Relative Roles of Climate, Soil, Vegetation and Topography in Determining Seasonal and Long‐Term Catchment Dynamics publication-title: Advances in Water Resources – volume: 132 start-page: 260 issue: 3–4 year: 2011 end-page: 271 article-title: Under What Conditions Do Parallel River Networks Occur? publication-title: Geomorphology – volume: 45 issue: 1 year: 2009 article-title: River Networks as Ecological Corridors: A Complex Systems Perspective for Integrating Hydrologic, Geomorphologic, and Ecologic Dynamics publication-title: Water Resources Research – volume: 63 issue: 1 year: 2000 article-title: Geometry of River Networks. III. Characterization of Component Connectivity publication-title: Physical Review E – volume: 27 start-page: 2319 issue: 12 year: 2013 end-page: 2342 article-title: Automatic Drainage Pattern Recognition in River Networks publication-title: International Journal of Geographical Information Science – volume: 16 start-page: 834 issue: 2 year: 2020 end-page: 846 article-title: Diverse Supraglacial Drainage Patterns on the Devon Ice Cap, Arctic Canada publication-title: Journal of Maps – volume: 8 start-page: 10 issue: 1 year: 2024 article-title: Impacts of Land Use/Land Cover Changes on the Hydrology of the Fafan Catchment Ethiopia publication-title: Journal of Geovisualization and Spatial Analysis – volume: 1 start-page: 57 year: 2020 end-page: 81 article-title: Graph Neural Networks: A Review of Methods and Applications publication-title: AI Open – volume: 53 start-page: 435 issue: 3 year: 2024 end-page: 449 article-title: An InSAR Phase Unwrapping Method Based on R2AU‐Net publication-title: Acta Geodaetica et Cartographica Sinica – volume: 6 start-page: 1 issue: 1 year: 2004 end-page: 16 article-title: Drainage Morphometry and Its Influence on Landform Characteristics in a Basaltic Terrain, Central India–a Remote Sensing and GIS Approach publication-title: International Journal of Applied Earth Observation and Geoinformation – volume: 45 start-page: 1960 issue: 12 year: 2020 end-page: 1969 article-title: Grid Pattern Recognition in Road Networks Based on Graph Convolution Network Model publication-title: Geomatics and Information Science of Wuhan University – volume: 36 start-page: 1119 issue: 6 year: 2022 end-page: 1139 article-title: Detecting Interchanges in Road Networks Using a Graph Convolutional Network Approach publication-title: International Journal of Geographical Information Science – volume: 6 start-page: 1 issue: 1 year: 2019 end-page: 23 article-title: Graph Convolutional Networks: A Comprehensive Review publication-title: Computational Social Networks – volume: 572 start-page: 66 year: 2019 end-page: 74 article-title: A New Approach for River Network Classification Based on the Beta Distribution of Tributary Junction Angles publication-title: Journal of Hydrology – volume: 39 start-page: 28 issue: 1 year: 2024 article-title: Regularizing Building Outlines Extracted From Remote Sensing Images by Integrating Multiple Algorithms publication-title: Geocarto International – volume: 8 start-page: 11 issue: 1 year: 2024 article-title: Automated Map Generalization: Emerging Techniques and New Trends publication-title: Journal of Geovisualization and Spatial Analysis – volume: 51 start-page: 2246 issue: 11 year: 1967 end-page: 2259 article-title: Drainage Analysis in Geologic Interpretation: A Summation publication-title: AAPG Bulletin – year: 2017 – volume: 54 start-page: 505 issue: 4 year: 1988 end-page: 509 article-title: Quantitative Description and Classification of Drainage Patterns publication-title: Photogrammetric Engineering and Remote Sensing – volume: 11 start-page: 5875 issue: 1 year: 2021 article-title: A Novel Approach to the Classification of Terrestrial Drainage Networks Based on Deep Learning and Preliminary Results on Solar System Bodies publication-title: Scientific Reports – volume: 30 start-page: 129 issue: 2 year: 2011 end-page: 150 article-title: Wavelets on Graphs via Spectral Graph Theory publication-title: Applied and Computational Harmonic Analysis – volume: 8 start-page: 19 issue: 1 year: 2024 article-title: Enhancing Flood Risk Analysis in Harris County: Integrating Flood Susceptibility and Social Vulnerability Mapping publication-title: Journal of Geovisualization and Spatial Analysis – year: 2013 – ident: e_1_2_10_6_1 doi: 10.1007/s41651‐024‐00181‐5 – ident: e_1_2_10_11_1 doi: 10.11947/j.AGCS.2024.20230305 – ident: e_1_2_10_38_1 doi: 10.1080/13658816.2021.2024195 – ident: e_1_2_10_49_1 doi: 10.1017/S002211200100427X – ident: e_1_2_10_40_1 doi: 10.1016/j.eswa.2022.118639 – ident: e_1_2_10_35_1 doi: 10.1080/13658816.2016.1205193 – ident: e_1_2_10_48_1 doi: 10.1016/j.aiopen.2021.01.001 – ident: e_1_2_10_3_1 doi: 10.1016/j.iswcr.2016.03.001 – ident: e_1_2_10_27_1 doi: 10.4324/9780203070123 – ident: e_1_2_10_5_1 doi: 10.4324/9780203371084 – ident: e_1_2_10_42_1 doi: 10.5194/isprsarchives‐XXXIX‐B2‐29‐2012 – ident: e_1_2_10_32_1 doi: 10.1016/S0309‐1708(02)00164‐1 – ident: e_1_2_10_47_1 doi: 10.1029/2022WR033681 – ident: e_1_2_10_9_1 doi: 10.1111/j.1466‐8238.2009.00486.x – ident: e_1_2_10_36_1 doi: 10.1016/j.isprsjprs.2019.02.010 – ident: e_1_2_10_12_1 doi: 10.1306/5D25C26D‐16C1‐11D7‐8645000102C1865D – ident: e_1_2_10_26_1 doi: 10.1016/j.jenvp.2007.07.001 – ident: e_1_2_10_2_1 doi: 10.1007/s41651‐024‐00172‐6 – ident: e_1_2_10_44_1 doi: 10.1016/j.patcog.2019.06.012 – ident: e_1_2_10_31_1 doi: 10.1080/17538947.2023.2172224 – ident: e_1_2_10_21_1 doi: 10.1029/98WR00983 – ident: e_1_2_10_25_1 doi: 10.1029/2008WR007124 – ident: e_1_2_10_41_1 doi: 10.1016/j.jag.2022.102696 – ident: e_1_2_10_16_1 – ident: e_1_2_10_33_1 doi: 10.1038/s43247‐022‐00663‐8 – ident: e_1_2_10_20_1 doi: 10.1029/2007JF000781 – ident: e_1_2_10_46_1 doi: 10.1007/s41651‐024‐00174‐4 – ident: e_1_2_10_24_1 doi: 10.1016/j.jag.2004.06.003 – ident: e_1_2_10_39_1 doi: 10.1080/10106049.2024.2370322 – ident: e_1_2_10_7_1 doi: 10.1103/PhysRevE.63.016117 – ident: e_1_2_10_37_1 doi: 10.1080/10095020.2023.2264337 – ident: e_1_2_10_18_1 doi: 10.1111/tgis.13041 – ident: e_1_2_10_43_1 doi: 10.1080/13658816.2013.802794 – ident: e_1_2_10_17_1 doi: 10.1016/j.geomorph.2020.107045 – ident: e_1_2_10_13_1 doi: 10.1029/93WR02279 – ident: e_1_2_10_15_1 doi: 10.1016/j.jhydrol.2019.02.041 – ident: e_1_2_10_22_1 doi: 10.1016/S0169‐555X(01)00036‐8 – ident: e_1_2_10_23_1 doi: 10.1130/0091‐7613(1987)15<813:EORSOD>2.0.CO;2 – ident: e_1_2_10_14_1 doi: 10.1016/j.geomorph.2011.05.014 – ident: e_1_2_10_29_1 doi: 10.1109/CVPR.2017.649 – ident: e_1_2_10_28_1 doi: 10.1130/G45608.1 – ident: e_1_2_10_8_1 doi: 10.1038/s41598‐021‐85254‐x – ident: e_1_2_10_10_1 doi: 10.1016/j.acha.2010.04.005 – ident: e_1_2_10_30_1 doi: 10.13203/j.whugis20200022 – ident: e_1_2_10_45_1 doi: 10.1186/s40649‐019‐0069‐y – ident: e_1_2_10_4_1 doi: 10.1029/RS023i003p00471 – ident: e_1_2_10_19_1 doi: 10.1080/17445647.2020.1838353 – ident: e_1_2_10_34_1 doi: 10.1016/j.earscirev.2022.104191
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Snippet	ABSTRACT Drainage pattern recognition plays a crucial role in flood management, hydraulic engineering site selection, and biodiversity maintenance. Although... Drainage pattern recognition plays a crucial role in flood management, hydraulic engineering site selection, and biodiversity maintenance. Although deep...
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SubjectTerms	Accuracy Artificial neural networks Biodiversity Chebyshev approximation Deep learning Drainage Drainage patterns Flood control Flood management Geographic information systems graph convolutional network Hydraulic engineering Machine learning Mathematical morphology Pattern recognition Polynomials river Site selection Support vector machines three‐dimensional elevation
Title	Drainage Pattern Recognition Method Using Graph Convolutional Networks Combined With Three‐Dimensional Elevation Features
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