lyh910926's repositories
AI4Water
framework for developing machine (and deep) learning models for structured data
APEX-SWAT-GW-model
The linked APEX-SWAT-GW model was constructed to transfer variable values between the APEX and SWAT-GW simulation subroutines.The linking of these two models is forced by transferring the PRK (APEX variable) to Wrchrg (SWAT-GW variable).
awesome-causality-algorithms
An index of algorithms for learning causality with data
BaseFlowCONUS
Estimating Gridded Monthly Baseflow From 1981 to 2020 for the Contiguous US Using Long Short-Term Memory (LSTM) Networks
CAS-Clumping-Index-Products
Google Earth Engine JS code produces the global Clumping Index
causalml
Uplift modeling and causal inference with machine learning algorithms
chatgpt_academic
科研工作专用ChatGPT拓展，特别优化学术Paper润色体验，支持自定义快捷按钮，支持markdown表格显示，Tex公式双显示，代码显示功能完善，新增本地Python工程剖析功能/自我剖析功能
DataScience-master
Machine Learning, Python, Deep Learning, Linux, Pandas, Matplotlib, Git...
DDPM_ver1.0
Distributed dynamic process model (DDPM) is a bidirectional coupling eco-hydrological model for (but not limited to) steppe inland river basins in arid and semi-arid regions, which is driven by meteorological data and developed by Dr. Mingyang Li and Prof. Tingxi Liu.
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
EASYMORE
EASYMORE; EArth SYstem MOdeling REmapper
HydroSight
Groundwater timeseries analysis of hydrographs
HydroSPDB
Streamflow Prediction in (Dammed) Basins (SPDB) with Deep Learning models
hydrus
水文水资源（Hydrology and Water Resources）方面使用python最基础教程
Machine-Learning-in-Hydrology
Repository for UNR class GEOL - 701T (Applications of Machine Learning in Hydrology)
metran
Multivariate timeseries analysis using dynamic factor modelling.
MVGC1
The MVGC Multivariate Granger Causality toolbox for Granger-causal inference from time-series data.
neuralhydrology
Python library to train neural networks with a strong focus on hydrological applications.
PINNs
PyTorch Implementation of Physics-informed Neural Networks
PyESD
Python Package for Empirical Statistical Downscaling. pyESD is under active development and all colaborators are welcomed. The purpose of the package is to downscale any climate variables e.g. precipitation and temperature using predictors from reanalysis datasets (eg. ERA5) to point scale. pyESD adopts many ML and AL as the transfer function.
python-resources-for-earth-sciences
A Curated List of Python Resources for Earth Sciences
R-SWAT
This is an interactive web-based app for parallel parameter sensitivity, calibration, and uncertainty analysis with the Soil and Water Assessment Tool
rat_v2
Satellite remote sensing based reservoir operations monitoring
rhodium-swmm
Rhodium-SWMM is a Python library for green infrastructure placement under deep uncertainty.
StressNet
Ecosystem-scale transpiration stress formulation using deep learning
SurEau-Ecos
-- SurEau-Ecos_v1.x.x --