Soil Water Erosion and Its Hydrodynamic Characteristics in Degraded Bald Patches of Alpine Meadows in the Yellow River Source Area, Western China

Tong, Shengchun; Li, Guorong; Li, Xilai; Li, Jinfang; Zhai, Hua‐Jin; Zhao, Jianyun; Hong, Zhu; Liu, Yabin; Chen, Wenting; Hu, Xiasong

doi:10.3390/su15108165

Cited by 2 publications

(2 citation statements)

References 59 publications

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“…Gray correlation analysis is a mathematical method used to calculate the correlation coefficient of two sequences by studying the geometric proximity between subsequences and parent sequences [75,76]. It is suitable for more accurately locating correlation characteristics in 'poor information' and 'gray relationships', where the sample size is small and the change law is partially known.…”

Section: Parameter Optimizationmentioning

confidence: 99%

Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

Zhao,

Bing

2024

Buildings

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Unconfined compressive strength (UCS) is an important parameter of rock and soil mechanical behavior in foundation engineering design and construction. In this study, salinized frozen soil is selected as the research object, and soil GDS tests, ultrasonic tests, and scanning electron microscopy (SEM) tests are conducted. Based on the classification method of the model parameters, 2 macroscopic parameters, 38 mesoscopic parameters, and 19 microscopic parameters are selected. A machine learning model is used to predict the strength of soil considering the three-level characteristic parameters. Four accuracy evaluation indicators are used to evaluate six machine learning models. The results show that the radial basis function (RBF) has the best UCS predictive performance for both the training and testing stages. In terms of acceptable accuracy and stability loss, through the analysis of the gray correlation and rough set of the three-level parameters, the total amount and proportion of parameters are optimized so that there are 2, 16, and 16 macro, meso, and micro parameters in a sequence, respectively. In the simulation of the aforementioned six machine learning models with the optimized parameters, the RBF still performs optimally. In addition, after parameter optimization, the sensitivity proportion of the third-level parameters is more reasonable. The RBF model with optimized parameters proved to be a more effective method for predicting soil UCS. This study improves the prediction ability of the UCS by classifying and optimizing the model parameters and provides a useful reference for future research on salty soil strength parameters in seasonally frozen regions.

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Section: Parameter Optimizationmentioning

confidence: 99%

Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

Zhao,

Bing

2024

Buildings

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“…The different hydrodynamic parameters representing the runoff flow process on the slope were taken to accurately describe the effect of changing the number of pika mounds on the hydrodynamic characteristics. The hydrodynamic parameters selected for this study included the runoff depth (H), mean flow velocity (v), Froude number (Fr), Reynolds number (Re), and Darcy-Weisbach resistance coefficient (f ); the specific formulae and their applicability are given in the citations [45,46].…”

Section: Indicator Testing and Analysismentioning

confidence: 99%

Influence of the Plateau Pika Mound Numbers on Soil Water Erosion Properties in Alpine Meadows of the Yellow River Source Zone, Western China

Tong,

Li,

et al. 2023

Water

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The plateau pika (Ochotona curzoniae) actively contributes to soil erosion and meadow degradation in western China’s Yellow River source zone. This study aimed to elucidate the effects of the pika mound numbers on the hydrodynamic characteristics and soil water erosion through simulated rainfall experiments. The inhibition effects of restored vegetation growth on meadow degradation and soil erosion were explored using a revegetated pika mound as a control. The results showed that at a rainfall intensity of 30 mm/h, the soil loss per unit time increased and then decreased with rainfall time and that 15–20 min of rainfall duration was the sensitive period for soil loss in different pika mound patch lands. The degradation of meadows due to the activities of plateau pika is an essential factor influencing soil erosion, and the soil erosion rate is positively correlated with both the pika mound numbers and slope. The mean flow velocity can better describe the process of the soil erosion, and its value increased with the number of pika mounds and slope. The Reynolds number ranged from 57.85 to 153.63 (Re <500), and it was preliminarily determined that all slope runoff was laminar flow. The Froude number increased linear function with pika mound numbers (p < 0.01), and both the pika mound numbers and slope were significant factors affecting it (p < 0.05). The Darcy–Weisbach resistance coefficient instead decreased with the pika mound numbers and slope, and the inhibitory effect of vegetation on soil erosion was probably limited when the mound numbers reached a certain level. According to the results of the grey correlation and Pearson correlation analysis, changes in the number of pika mounds led to variability in the soil water erosion properties by altering the landscape scale effect. The number of pika mound patches (NP), edge length index (TE), area index (AREA), and volume index (V) were the key influencing factors on the soil erosion rate and hydrodynamic parameters. We conclude that plateau pika activities intensify meadow degradation, and the continuous increase in pika mounds decreases the vegetation cover and flow resistance and increases soil erodibility. Controlling the intensity of plateau pika activities will effectively prevent soil erosion in degraded areas.

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Soil Water Erosion and Its Hydrodynamic Characteristics in Degraded Bald Patches of Alpine Meadows in the Yellow River Source Area, Western China

Cited by 2 publications

References 59 publications

Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

Influence of the Plateau Pika Mound Numbers on Soil Water Erosion Properties in Alpine Meadows of the Yellow River Source Zone, Western China

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