Journal of Geo-information Science ›› 2023, Vol. 25 ›› Issue (5): 909-923.doi: 10.12082/dqxxkx.2023.220712

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Geomorphic Recognition of China Considering Complex Network of Catchments

QI Meng1,2(), CHEN Nan1,2,*(), LIN Siwei3, ZHOU Qianqian4   

  1. 1. Key Lab for Spatial Data Mining and Information Sharing of Education Ministry, Fuzhou University, Fuzhou 350108, China
    2. The Academy of Digital China, Fuzhou University, Fuzhou 350108, China
    3. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
    4. College of computer and data science, Fuzhou University, Fuzhou 350108, China
  • Received:2022-09-21 Revised:2022-12-14 Online:2023-05-25 Published:2023-04-27
  • Contact: CHEN Nan;
  • Supported by:
    National Natural Science Foundation of China(41771423)


Landform recognition has become a key part of geomorphological research, which has been widely concerned by scholars. The research of geomorphic units based on catchment has become a hotspot in the field of landform recognition. Previous studies have generated a series of new questions, such as whether large-scale landform types can be identified based on local catchment landform features, which landform description methods are more adaptable, and what is the knowledge bottleneck of current landform recognition methods based on the catchment. So, in this paper, we selected sample areas representing five major landform types in China, including karst, loess, periglacial, aeolian, and fluvial. Based on the complex network theory, we took the complex network indicators and the topographic metrics as the basic data sources. Three typical machine learning methods, i.e., LightGBM, XGBoost, and RF, were used to automatically identify the main geomorphic types in China. Results show that both the complex network structure and the terrain features of the catchment have certain explanatory power and recognition effect on landforms, and the overall recognition accuracy is 77.5% and 72.5%, respectively. Among the five geomorphologic types selected, LightGBM, XGBoost, and RF machine learning methods have the highest recognition accuracy (up to 100%) on periglacic geomorphology. Compared to a single geomorphic description data source, the geomorphic recognition effect that combines the two data sources is significantly improved. The overall accuracy using two data sources is 5% and 10% higher than that using the single complex network dataset and the single topographic dataset, respectively. Moreover, LightGBM has better adaptability to the combination of complex network and terrain factor feature sets, and the overall accuracy can reach 82.5%. In general, this study expands the application area and scope of catchment landform recognition methods, and provides a new idea for the research of catchment landform recognition.

Key words: China, landform recognition, watershed, DEM, complex network, terrain feature, machine learning