zhouyunzhouyun

zhouyunzhouyun's Stars

• Nowosad/global_population_and_gdp

  Global dataset of gridded population and GDP (1980-2010 estimations and 2020-2100 scenarios)

  Language:R174

• ecmwf/thermofeel

  thermofeel is a library to calculate human thermal comfort indexes

  Language:Python6719

• nimaa66/Crop-Yield-Estimation

  I employ an advanced approach combining Google Earth Engine (GEE) and data from the MODIS satellite to gather comprehensive remote sensing data. This data is then analyzed using cutting-edge Machine Learning Algorithms, specifically XGBoost and Random Forest (RF), to accurately predict crop yields within a cultivation season.

  Language:Python6

• samapriya/awesome-gee-community-datasets

  Community Datasets added by users and made available for use at large

  Language:HTML788128

• njleach/PNAS-Leach-2021

  Repository containing the notebooks used for the forecast-based attribution analysis of the 2019 European winter heatwave.

  Language:Jupyter Notebook61

• dair-ai/ml-visuals

  🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

  13.6k1.4k

• ecmwf-projects/mooc-machine-learning-weather-climate

  Language:Jupyter Notebook187166

• ElsevierSoftwareX/SOFTX-D-21-00124

  thermofeel is a library to calculate human thermal comfort indexes. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711022000176

  41

• wpgp/popRF

  Random Forest-informed Population Disaggregation R package

  Language:R302

• leizhang-geo/CNN-LSTM_for_DSM

  Using CNN-LSTM deep learning model for digital soil mapping. This is the code for paper "Zhang et al. A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables"

  Language:Python5413

• carbonplan/cmip6-downscaling

  Climate downscaling using CMIP6 data

  Language:Jupyter Notebook17727

• geogismx/Deep-learning-for-multi-year-ENSO-Reproduction

  Deep learning for multi-year ENSO forecasts Reproduction

  1

• samapriya/geeup

  Simple CLI for Google Earth Engine Uploads

  Language:Python12026

• aws-samples/aws-mlu-explain

  Visual, Interactive Articles About Machine Learning: https://mlu-explain.github.io/

  Language:JavaScript58979

• interpretml/interpret

  Fit interpretable models. Explain blackbox machine learning.

  Language:C++6.3k729

• QINQINKONG/PyWBGT

  Python code for calculating Liljegren WBGT

  Language:Jupyter Notebook83

• tammasloughran/ehfheatwaves

  A script to calculate heatwaves from AWAP

  Language:Python305

• whulixiya/Civil-war-hinders-crop-production-and-threatens-food-security-in-Syria

  Language:MATLAB21

• geogismx/Heatwavetracker

  Language:JavaScript104

• shap/shap

  A game theoretic approach to explain the output of any machine learning model.

  Language:Jupyter Notebook22.8k3.3k

• dataman-git/codes_for_articles

  Language:Jupyter Notebook168167

• janewbaldwin/Compound-Heat-Waves

  Code supporting publication in journal Earth's Future, Baldwin et al 2019 "Temporally Compound Heat Waves and Global Warming: An Emerging Hazard."

  Language:Jupyter Notebook3

• jakevdp/PythonDataScienceHandbook

  Python Data Science Handbook: full text in Jupyter Notebooks

  Language:Jupyter Notebook43.2k17.9k

• jakevdp/WhirlwindTourOfPython

  The Jupyter Notebooks behind my OReilly report, "A Whirlwind Tour of Python"

  Language:Jupyter Notebook3.7k1.6k

• ecohydro/GlobalUrbanHeat

  Repository for Tuholske, C., Caylor, K., Funk, C., Verdin, A., Sweeney, S., Grace, K., ... & Evans, T. (2021). Global urban population exposure to extreme heat. Proceedings of the National Academy of Sciences, 118(41).

  Language:Jupyter Notebook319