dMC-Juniata-hydroDL River Graph Dataset

NIAID Data Ecosystem2026-05-01 收录

水文深度学习

河流图建模

数据链接：

https://zenodo.org/record/10183428 数据链接链接失效反馈

官方服务：

资源简介：

This release is the data for the dMC-Juniata-hydroDL2 project (https://zenodo.org/doi/10.5281/zenodo.10183448). There is a readme inside of the .zip file which contains instructions for how to use this dataset.

创建时间：

2023-11-22

Hydro-metorological data about the upstream of Huaxian&Linjiacun hydrometric station (0-1 normalized) and paper related code

径流模拟

水文深度学习

Data about"Improving Rainfall-Runoff Simulation in Semi-Arid Basins by Combining Data Segmentation and Attention-Based Deep Learning"According to the confidentiality clause, the original data should n

Figshare2023-08-13 更新40

LSTM-REG: regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks

径流模拟

水文深度学习

LSTM-REG: regional gridded runoff dataset for northwest Russia. Model: LSTM. Geographical domain: 25–57°E; 55–70°N. Spatial resolution: 0.5°x0.5°. Temporal resolution: daily. Period: 1980-2016.

Zenodo2020-12-11 更新20

LSTM-REG: regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks

径流模拟

水文深度学习

Zenodo2020-12-11 更新00

LSTM-based data integration to improve Snow Water Equivalent prediction and...

雪水当量预测

水文深度学习

Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Re-cently, deep learning methods such as long short-term memory (LSTM) have...

University Corporation for Atmospheric Research30

Are deep learning models in hydrology entity aware?

水文深度学习

感知模型

This is the dataset and code accompanying the publication "Are deep learning models in hydrology entity aware?" of Benedikt Heudorfer, Hoshin V. Gupta, and Ralf Loritz to allow reproducible results.

NIAID Data Ecosystem10

dMC-Juniata-hydroDL River Graph Dataset

资源简介：

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