引入集水区复杂网络的中国地貌识别研究

doi:10.12082/dqxxkx.2023.220712

Abstract

Abstract:

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

QI Meng, CHEN Nan, LIN Siwei, ZHOU Qianqian. Geomorphic Recognition of China Considering Complex Network of Catchments[J].Journal of Geo-information Science, 2023, 25(5): 909-923.DOI:10.12082/dqxxkx.2023.220712

Figures/Tables 13

Fig. 1

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Tab. 1

Description of complex network indicators and related calculation methods

指标名称	指标公式	公式参数说明	地学意义	公式编号
平均度	$k - = 1 n ∑ i = 1 n k i$	$k - 为平均度； k i$ 为复杂网络中第i个网络节点的度数；n是集水区复杂网络中的节点总数	反映了集水区网络中某一类型节点所占的比重	（1）
平均加权度	$K - = ∑ i K i / N$	$K - 为平均加权度； K i$ 为复杂网络中第i条网络边的权重；N为集水区复杂网络中的节点总数	反映了集水区复杂网络中某一类型节点对应边的权重情况	（2）
网络直径	$N D = ∑ i j m a x (d i j)$	$N D$ 为网络直径；集水区复杂网络中节点i到j的距离 $d i j$ 为连接这两个节点间最短路径的边数	表达了网络中各节点之间的距离	（3）
网络密度	$d G = 2 m / [n (n - 1)]$	在集水区复杂网络中，用网络中实际拥有的连线书与最多可能存在的连线总数之比表示 $d G$ 网络密度，m是网络中实际拥有的连接数；n是网络节点总数	反映了集水区复杂网络中各节点间联络的紧密程度	（4）
平均路径长度	$L = 2 N (N - 1) ∑ i > j d i j$	对于一个集水区复杂网络，2个特征点之间存在多条集水区特征点连通且存在多种连通方式，则两个特征节点i与j之间的距离 $d i j$ 就是两节点之间最短路径经过的其他集水区特征节点的平均数量。 $N$ 为网络节点总数， $L$ 为平均路径长度	反映了复杂的集水区网络中节点之间的分离程度	（5）
网络结构熵	$E = - ∑ i = 1 n d (i) ∑ i = 1 n d (i) l n H i$	$E 为网络结构熵； H i$ 为第i个节点的重要度；n为网络节点总数； $d (i)$ 表示第i节点的度	从整体角度衡量了集水区复杂网络系统演化的发展趋势	（6）
分形维数	$d = - l i m r > 0 l n N (r) l n r$	对于任意一个集水区复杂网络，采用边长为r的正方形盒子覆盖，会出现一些盒子是包含图形，其余为空盒子的情况。随着盒子边长的增加，则包含图形的盒子数目逐渐减少。 $d 为分形维数；$ 将边长为r（r=1,2,3,…,M）的正方体覆盖网络，会出现包含图形的盒子和空盒子 $N (r)$ 。在适当的范围内，对r选取一系列不同的值，以 $l n r$ 为横坐标， $l n N (r)$ 为纵坐标，利用最小二乘法对其进行线性回归，对其斜率取负即为集水区复杂网络的分形维数	刻画了集水区复杂网络系统的复杂程度，反映了集水区复杂网络系统的相似性	（7）

Tab. 1

Tab. 2

Topographic index meaning and related calculation methods

指标名称	指标公式	公式参数说明	地学含义	公式编号
坡向/° （Aspect）	$A s p e c t = a r c t a n f y f x$	$f x$ 为东西方向上的高程变化率； $f y$ 为南北方向上的高程变化率	描述了集水区上坡面的朝向	（8）
坡度/% （slope）	$s l o p e = a r c t a n f x 2 + f y 2 × 180 π$	$f x$ 为东西方向上的高程变化率； $f y$ 为南北方向上的高程变化率	描述了集水区地表单元的陡缓程度	（9）
粗糙度（SR）	$S R = 1 c o s s l o p e × 3.14 180$	$s l o p e$ 为集水区地形坡度	描述了集水区地势起伏的复杂程度	（10）
坡向变率（SOA）	$S O A = S O A 1 - S O A 2 - \| S O A 1 - S O A 2 \| 2$	$S O A 1$ 为正地形计算的坡向变率； $S O A 2$ 为负地形计算的坡向变率	描述了地表局部范围内坡向的变化情况	（11）
平面曲率（Curve）	$C = f x x f y 2 - 2 f x y f x f y + f y y f x 2 (f x 2 + f y 2) 3 / 2$	$f x$ 为东西方向上的高程变化率； $f y$ 为南北方向上的高程变化率； $f x x$ 是东西方向上的高程变化率的变化率； $f x y$ 是东西方向的高程变化率在南北方向的变化率； $f y y$ 是南北方向的高程变化率的变化率	集水区地形曲面在水平方向的曲率，描述了地面等高线的弯曲程度	（12）
剖面曲率（SOS）	$S O S = f x x f x 2 + 2 f x y f x f y + f y y f y 2 (f x 2 + f y 2) (1 + f x 2 + f y 2) 3 / 2$		描述了坡度的变化程度	（13）
地表切割深度/m （SCD）	$S C D = H m e a n - H m i n$	$H m e a n$ 为集水区高程的平均值； $H m i n$ 为该集水区最小高程值	反映了地表被侵蚀切割的情况	（14）
高程变异系数（ECV）	$E C V = H s t d H m e a n$	$H s t d$ 为集水区高程标准差； $H m e a n$ 为该集水区高程的平均值	反映了地貌特征的差异性	（15）

Tab. 2

Fig. 7

Tab. 3

Tab. 4

Tab. 5

Fig. 8

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Geomorphic Recognition of China Considering Complex Network of Catchments

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	岩溶地貌	黄土地貌	冰缘地貌	风成地貌	流水地貌	精准度/%	召回率/%	F1值
岩溶地貌	5	1		1		71	83	77
黄土地貌	1	5		1		71	83	77
冰缘地貌			8			100	80	90
风成地貌				7	1	88	78	83
流水地貌			2		6	75	86	81
总体精度/%	77.5

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