scholarly journals Soil Erosion Assessment of Alpine Grassland in the Source Park of the Yellow River on the Qinghai-Tibetan Plateau, China

2022 ◽  
Vol 9 ◽  
Author(s):  
Huilong Lin ◽  
Yuting Zhao
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Tibetan Plateau ◽  
Variable Selection ◽  
Yellow River ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Yellow River ◽  
Optimal Model ◽  
Erosion Loss

The source park of the Yellow River (SPYR), as a vital ecological shelter on the Qinghai-Tibetan Plateau, is suffering different degrees of degradation and desertification, resulting in soil erosion in recent decades. Therefore, studying the mechanism, influencing factors and current situation of soil erosion in the alpine grassland ecosystems of the SPYR are significant for protecting the ecological and productive functions. Based on the 137Cs element tracing technique and machine learning algorithms, five strategic variable selection algorithms based on machine learning algorithms are used to identify the minimal optimal set and analyze the main factors that influence soil erosion in the SPYR. The optimal model for estimating soil erosion in the SPYR is obtained by comparisons model outputs between the RUSLE and machine learning algorithms combined with variable selection models. We identify the spatial distribution pattern of soil erosion in the study area by the optimal model. The results indicated that: (1) A comprehensive set of variables is more objective than the RUSLE model. In terms of verification accuracy, the simulated annealing -Cubist model (R = 0.67, RMSD = 1,368 t km–2⋅a–1) simulation results represents the best while the RUSLE model (R = 0.49, RMSD = 1,769 t⋅km–2⋅a–1) goes on the worst. (2) The soil erosion is more severe in the north than the southeast of the SPYR. The average erosion modulus is 6,460.95 t⋅km–2⋅a–1 and roughly 99% of the survey region has an intensive erosion modulus (5,000–8,000 t⋅km–2⋅a–1). (3) Total erosion loss is relatively 8.45⋅108 t⋅a–1 in the SPYR, which is commonly 12.64 times greater than the allowable soil erosion loss. The economic monetization of SOC loss caused by soil erosion in the entire research area was almost $47.90 billion in 2014. These results will help provide scientific evidences not only for farmers and herdsmen but also for environmental science managers and administrators. In addition, a new ecological policy recommendation was proposed to balance grassland protection and animal husbandry economic production based on the value of soil erosion reclassification.

scholarly journals Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1073 ◽  
Author(s):  
Li ◽  
Li ◽  
Li ◽  
Liu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Variable Selection ◽  
Aboveground Biomass ◽  
Forest Type ◽  
Learning Algorithms ◽  
Forest Biomass ◽  
Machine Learning Algorithms ◽  
Biomass Estimation ◽  
Landsat 8

Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensing-based forest biomass estimation in general is well developed and extensively used, improving the accuracy of biomass estimation remains challenging. In this paper, we used China’s National Forest Continuous Inventory data and Landsat 8 Operational Land Imager data in combination with three algorithms, either the linear regression (LR), random forest (RF), or extreme gradient boosting (XGBoost), to establish biomass estimation models based on forest type. In the modeling process, two methods of variable selection, e.g., stepwise regression and variable importance-base method, were used to select optimal variable subsets for LR and machine learning algorithms (e.g., RF and XGBoost), respectively. Comfortingly, the accuracy of models was significantly improved, and thus the following conclusions were drawn: (1) Variable selection is very important for improving the performance of models, especially for machine learning algorithms, and the influence of variable selection on XGBoost is significantly greater than that of RF. (2) Machine learning algorithms have advantages in aboveground biomass (AGB) estimation, and the XGBoost and RF models significantly improved the estimation accuracy compared with the LR models. Despite that the problems of overestimation and underestimation were not fully eliminated, the XGBoost algorithm worked well and reduced these problems to a certain extent. (3) The approach of AGB modeling based on forest type is a very advantageous method for improving the performance at the lower and higher values of AGB. Some conclusions in this paper were probably different as the study area changed. The methods used in this paper provide an optional and useful approach for improving the accuracy of AGB estimation based on remote sensing data, and the estimation of AGB was a reference basis for monitoring the forest ecosystem of the study area.

An Insight into Machine Learning Algorithms to Map the Occurrence of the Soil Mattic Horizon in the Northeastern Qinghai-Tibetan Plateau

Pedosphere ◽  
2018 ◽  
Vol 28 (5) ◽  
pp. 739-750 ◽  
Author(s):  
Junjun ZHI ◽  
Ganlin ZHANG ◽  
Renmin YANG ◽  
Fei YANG ◽  
Chengwei JIN ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tibetan Plateau ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Insight Into

scholarly journals Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaolu Tian ◽  
Yutian Chong ◽  
Yutao Huang ◽  
Pi Guo ◽  
Mengjie Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatitis B ◽  
Surface Antigen ◽  
Hepatitis B Surface Antigen ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Optimal Model ◽  
Hbsag Seroclearance ◽  
Extreme Gradient Boosting

Hepatitis B surface antigen (HBsAg) seroclearance during treatment is associated with a better prognosis among patients with chronic hepatitis B (CHB). Significant gaps remain in our understanding on how to predict HBsAg seroclearance accurately and efficiently based on obtainable clinical information. This study aimed to identify the optimal model to predict HBsAg seroclearance. We obtained the laboratory and demographic information for 2,235 patients with CHB from the South China Hepatitis Monitoring and Administration (SCHEMA) cohort. HBsAg seroclearance occurred in 106 patients in total. We developed models based on four algorithms, including the extreme gradient boosting (XGBoost), random forest (RF), decision tree (DCT), and logistic regression (LR). The optimal model was identified by the area under the receiver operating characteristic curve (AUC). The AUCs for XGBoost, RF, DCT, and LR models were 0.891, 0.829, 0.619, and 0.680, respectively, with XGBoost showing the best predictive performance. The variable importance plot of the XGBoost model indicated that the level of HBsAg was of high importance followed by age and the level of hepatitis B virus (HBV) DNA. Machine learning algorithms, especially XGBoost, have appropriate performance in predicting HBsAg seroclearance. The results showed the potential of machine learning algorithms for predicting HBsAg seroclearance utilizing obtainable clinical data.

Assessing soil erosion and control factors by radiometric technique in the source region of the Yellow River, Tibetan Plateau

2014 ◽  
Vol 81 (3) ◽  
pp. 538-544 ◽  
Author(s):  
Yibo Wang ◽  
Fujun Niu ◽  
Qingbai Wu ◽  
Zeyong Gao
Keyword(s):  
Soil Erosion ◽  
Tibetan Plateau ◽  
Vegetation Cover ◽  
Soil Loss ◽  
Yellow River ◽  
Source Region ◽  
The Yellow River ◽  
The Tibetan Plateau ◽  
Erosion Rates ◽  
Main Factors

AbstractMeasurements of 137Cs concentration in soils were made in a representative catchment to quantify erosion rates and identify the main factors involved in the erosion in the source region of the Yellow River in the Tibetan Plateau. In order to estimate erosion rates in terms of the main factors affecting soil loss, samples were collected taking into account the slope and vegetation cover along six selected transects within the Dari County catchment. The reference inventory for the area was established at a stable, well-preserved, site of small thickness (value of 2324 Bq·m− 2). All the sampling sites had been eroded and 137Cs inventories varied widely in the topsoil (14.87–25.56 Bq·kg− 1). The effective soil loss values were also highly variable (11.03–28.35 t·km− 1·yr− 1) in line with the vegetation cover change. The radiometric approach was useful in quantifying soil erosion rates and examining patterns of soil movement.

scholarly journals Spatio-Temporal Variation Characteristics of Aboveground Biomass in the Headwater of the Yellow River Based on Machine Learning

2021 ◽  
Vol 13 (17) ◽  
pp. 3404
Author(s):  
Rong Tang ◽  
Yuting Zhao ◽  
Huilong Lin
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Aboveground Biomass ◽  
Yellow River ◽  
Machine Learning Algorithms ◽  
Random Forest Model ◽  
Accurate Estimation ◽  
The Yellow River ◽  
Research Results ◽  
Forest Model

Accurate estimation of the aboveground biomass (AGB) of grassland is a key link in understanding the regional carbon cycle. We used 501 aboveground measurements, 29 environmental variables, and machine learning algorithms to construct and verify a custom model of grassland biomass in the Headwater of the Yellow River (HYR) and selected the random forest model to analyze the temporal and spatial distribution characteristics and dynamic trends of the biomass in the HYR from 2001 to 2020. The research results show that: (1) the random forest model is superior to the other three models (R2val = 0.56, RMSEval = 51.3 g/m2); (2) the aboveground biomass in the HYR decreases spatially from southeast to northwest, and the annual average value and total values are 176.8 g/m2 and 20.73 Tg, respectively; (3) 69.51% of the area has shown an increasing trend and 30.14% of the area showed a downward trend, mainly concentrated in the southeast of Hongyuan County, the northeast of Aba County, and the north of Qumalai County. The research results can provide accurate spatial data and scientific basis for the protection of grassland resources in the HYR.

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