Spatial distribution characteristics of loose bodies in Huaishu River levee and their influence on seepage field

Author(s):  
Shan Gao ◽  
Yuan Wang ◽  
Sheng Liu

At present, many levees contain a large number of loose bodies as a result of poor-quality construction, biological damage, and other factors. In this context, loose bodies refer to soil with a relative density less than a specified value. Taking the Huaishu River levee in China as an example, this paper studies the distribution characteristics of loose bodies in the levee using statistical methods. First, ground-penetrating radar and other geophysical exploration methods are used to investigate loose bodies in the levee. The frequency distribution and Shapiro–Wilk method are then employed to study the distribution characteristics of the area and depth of loose bodies. The influence of loose bodies on the seepage field of the levee is then examined considering the spatial distribution of the loose bodies. It was found that the areas of loose bodies in the western and eastern upstream slope obey a logarithmic normal distribution. In the shallow layer (depth between 0–1 m), loose bodies appear relatively frequently, and the frequency initially increases with depth before decreasing. The maximum hydraulic gradient of the levee initially increases and then decreases as the depth of the loose body increases.

2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.


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