This tutorial introduces PiggyCast, an ensemble machine learning model designed to improve weather prediction accuracy by stacking forecasts from various numerical, AI-based, and hybrid weather prediction models.

In this tutorial, we learn about an advanced strategy for information retrieval for question answering with Large Language Models (LLMs) in knowledge-intensive domains like sustainability reporting.

At Climate Policy Radar, we’re building an open-source knowledge graph for climate policy. Using an ontology defined by climate policy experts, we create a set of machine learning models to highlight where each concept is mentioned in a comprehensive dataset of the world's climate laws, policies, and related documents.

By the end of the tutorial, participants will understand how different satellite data sources and modeling approaches influence flood mapping outcomes, and how these methods can support disaster response and long-term climate resilience.

This tutorial demonstrates how to generate field boundaries globally using the Fields of The World dataset, pretrained models, and command line interface (CLI). We then show how to use those boundaries in agricultural monitoring tasks under climate change, including crop type classification and forest loss monitoring.

Using a real-world dataset of hourly meter and weather data, participants will learn to build a robust counterfactual energy baseline with a LightGBM (Gradient Boosting Machine) model.

In climate science, explainable artificial intelligence (XAI) can be used to improve and validate deep learning methods, but evaluation and selection of XAI methods is challenging. Learn how to use the explainable AI evaluation package Quantus to compare and select an appropriate XAI for your climate AI research task.

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.

This tutorial introduces the CommonPower library, designed to benchmark safe reinforcement learning (RL) algorithms on control problems for power systems. We highlight two crucial issues in RL for power system control: safeguarding RL decision-making and assessing the impact of forecast quality on control performance.

The goal of this tutorial is to show how we can use methods from constraint-based causal discovery to uncover the causal relationships that are present in different moisture regimes. In doing that, we aim to improve our general understanding of the dynamics of extreme events.