Skip to content
@climatechange-ai-tutorials

climatechange-ai-tutorials

Popular repositories Loading

  1. nlp-policy-analysis nlp-policy-analysis Public

    Explore how Natural Language Processing (NLP) can be used to assist in identifying and mapping climate-relevant literature using a supervised learning approach and leverage a state of the art Large…

    Jupyter Notebook 52 90

  2. lulc-classification lulc-classification Public

    Mapping the extent of land use and land cover categories over time is essential for better environmental monitoring, urban planning and nature protection. Train and fine-tune a deep learning model …

    Jupyter Notebook 30 15

  3. optimal-power-flow optimal-power-flow Public

    AC Optimal Power Flow (OPF) attempts to determine the setpoints of generators that would minimize the operating cost of a power system while meeting other operational constraints. In this tutorial,…

    Jupyter Notebook 19 26

  4. building-control-boptest building-control-boptest Public

    Apply reinforcement learning to a building emulator to intelligently control HVAC systems.

    Jupyter Notebook 14 6

  5. climatelearn climatelearn Public

    Apply machine learning to predict climate variables into the future and transform low-resolution outputs of climate models into high-resolution regional forecasts.

    Jupyter Notebook 14 9

  6. bioacoustic-monitoring bioacoustic-monitoring Public

    This tutorial presents an "agile modeling" approach that enables users to build custom classifier systems efficiently for species of interest using transfer learning, audio search, and human-in-the…

    Jupyter Notebook 12 26

Repositories

Showing 10 of 38 repositories
  • optimal-power-flow Public

    AC Optimal Power Flow (OPF) attempts to determine the setpoints of generators that would minimize the operating cost of a power system while meeting other operational constraints. In this tutorial, learn how to leverage PyTorch to train a neural network to approximate the optimal solutions.

    climatechange-ai-tutorials/optimal-power-flow's past year of commit activity
    Jupyter Notebook 19 MIT 26 0 0 Updated Aug 26, 2026
  • climatechange-ai-tutorials/hurricane-wind-var's past year of commit activity
    Jupyter Notebook 1 21 0 0 Updated Aug 26, 2026
  • flood-monitoring Public

    Floods in coastal areas can be extremely destructive natural hazards resulting in societal and economical damage. In this tutorial, explore how to predict building and population density to understand the potential impact from a flooding event.

    climatechange-ai-tutorials/flood-monitoring's past year of commit activity
    Jupyter Notebook 6 MIT 28 0 0 Updated Aug 17, 2026
  • bioacoustic-monitoring Public

    This tutorial presents an "agile modeling" approach that enables users to build custom classifier systems efficiently for species of interest using transfer learning, audio search, and human-in-the-loop active learning.

    climatechange-ai-tutorials/bioacoustic-monitoring's past year of commit activity
    Jupyter Notebook 12 MIT 26 0 1 Updated Aug 17, 2026
  • tracking-ml-emissions Public

    Learn how to measure a machine learning model's carbon footprint and practice strategies that can help shrink the energy involved in training these models.

    climatechange-ai-tutorials/tracking-ml-emissions's past year of commit activity
    Jupyter Notebook 6 MIT 31 0 0 Updated Aug 17, 2026
  • climatechange-ai-tutorials/hands-on-outbreak-analytics's past year of commit activity
    Jupyter Notebook 1 MIT 14 0 0 Updated Aug 15, 2026
  • climatechange-ai-tutorials/lkotkote-bird-bioacoustics's past year of commit activity
    Jupyter Notebook 1 14 0 0 Updated Aug 14, 2026
  • coal-power-mrv Public

    Explore how to monitor coal power plant activity by leveraging satellite imagery and computer vision models.

    climatechange-ai-tutorials/coal-power-mrv's past year of commit activity
    Jupyter Notebook 4 MIT 22 0 0 Updated Aug 8, 2026
  • camels-hydrological-modeling Public

    A guide to model hydrological system using the real-world CAMELS dataset, which contains weather drivers for 531 basins across the continental United States. Through this modeling process, we will demonstrate various methods to predict streamflow, aiding in flood and drought planning.

    climatechange-ai-tutorials/camels-hydrological-modeling's past year of commit activity
    Jupyter Notebook 4 MIT 35 0 0 Updated Jul 27, 2026
  • citylearn Public

    Learn how to design simple and advanced control algorithms to provide energy flexibility, and acquire familiarity with the CityLearn environment and its datasets for extended use in projects. The tutorial provides a walk-through on how to set up and interact with the environment using a real-world dataset in three hands-on control experiments.

    climatechange-ai-tutorials/citylearn's past year of commit activity
    Jupyter Notebook 7 MIT 38 0 1 Updated Jul 27, 2026

People

This organization has no public members. You must be a member to see who’s a part of this organization.

Top languages

Jupyter Notebook