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China's transboundary water resources estimation using machine learning approaches

NIAID Data Ecosystem2026-03-11 收录

跨境水资源估算

机器学习环境预测

数据链接：

https://zenodo.org/record/1403295 数据链接链接失效反馈

官方服务：

资源简介：

China's transboundary water resources estimated using machine learning models (random forest, gradient boosting, and stacking).

应用场景：

创建时间：

2020-01-24

土壤属性空间制图

机器学习环境预测

The soil bulk density (BD) maps were developed using the quantile random forest machine learning algorithm, integrating remotely sensed and derived environmental covariates including terrain, climate,

Zenodo2026-06-24 更新90

Data sources.

噪音评估

机器学习环境预测

The evaluation of urban noise suitability is crucial for urban environmental management. Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study in

Figshare2024-12-23 更新40

The data for the manuscript "Characterizing water-sediments dynamics of dissolved organic matter in artificial reservoirs and natural lakes through machine learning approaches"

森林土壤碳储量

机器学习环境预测

The data is for the manuscript " Unraveling the distribution of black carbon in Chinese forest soils using machine learning approaches".

NIAID Data Ecosystem40

Machine Learning Estimates of Global Marine Nitrogen Fixation

海洋固氮速率估算

机器学习环境预测

Marine nitrogen fixation rates were estimated by two machine learning algorithms (random forest and support vector regression). The two algorithms were trained based on the measurements of nitrogen fi

Zenodo2021-02-14 更新60

China'S Transboundary Water Resources Estimation Using Machine Learning Approaches

跨境水资源估算

水文机器学习

China's transboundary water resources estimated using machine learning models (random forest, gradient boosting, and stacking).

Zenodo2020-09-20 更新40