相关实验视频

Updated: Jan 7, 2026

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.7K

在国家一级对土壤有机碳的可解释的数据驱动空间预测

Azamat Suleymanov1, Evgeny Abakumov2, Igor Savin3

  • 1Department of Applied Ecology, Saint-Petersburg State University, 199178, Saint-Petersburg, Russia; Laboratory of Soil Science, Ufa Institute of Biology, Ufa Federal Research Centre, Russian Academy of Sciences, 450054, Ufa, Russia.

The Science of the total environment
|December 28, 2025
PubMed
概括

这项研究使用可解释的机器学习在俄罗斯绘制了土壤有机碳 (SOC) 地图. 北部地区由于有机土壤和泥炭地,受到温度和降水的影响而显示出高的SOC.

关键词:
共同变量 共同变量 共同变量数字土壤绘制地图可以解释性 解释性机器学习 机器学习随机的森林随机的森林沙普利的价值是什么意思土壤有机碳土壤有机碳

更多相关视频

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management08:09

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Published on: September 12, 2017

12.2K
Measurements of Soil Carbon by Neutron-Gamma Analysis in Static and Scanning Modes07:51

Measurements of Soil Carbon by Neutron-Gamma Analysis in Static and Scanning Modes

Published on: August 24, 2017

7.7K

相关概念视频

Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.8K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
7.7K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
243
Levels of Use of a GIS01:29

Levels of Use of a GIS

Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
313