Machine Learning Case Note: Logistic Regression and Machine Learning in R for Climate Change with End-to-End Case Studies

Climate change has shifted from a theoretical concern to a lived reality, reshaping weather patterns, ecosystems, and the way we think about risk. Heatwaves are becoming more frequent in densely populated cities, coastal communities are facing higher flood risk due to sea-level rise and intense rainfall, and landscapes are increasingly vulnerable to devastating wildfires. Policymakers, planners, insurers, and scientists all need more than anecdotes or isolated events; they need robust, quantitative tools that can turn complex environmental drivers into probabilities that can be understood, compared, and acted upon.

Many of the key questions in climate risk are binary at their core. Did a heatwave occur on this day or not? Did a coastal cell flood this year or remain dry? Did a region experience a wildfire this season or avoid one? Logistic regression is a natural fit for this kind of problem. It connects predictors—such as temperature, humidity, sea-level anomalies, drought indices, soil moisture, and human activity—to the probability that an event occurs. Unlike more opaque black-box models, logistic regression provides interpretable coefficients and odds ratios, making it easier to explain model behaviour to stakeholders, document assumptions, and satisfy governance or regulatory requirements.

At the same time, climate-change analysis increasingly operates within a machine-learning workflow. Data now arrive as large gridded products, remote-sensing observations, or long time series derived from reanalyses and climate models. This demands a disciplined approach: careful data preparation, exploratory data analysis (EDA) to understand distributions and correlations, feature scaling, model training and validation, and rich diagnostic plots to assess discrimination and calibration. R provides a powerful environment for this, combining statistical rigour with flexible plotting and data manipulation tools.

This guide brings these elements together through three end-to-end simulated case studies in R: urban heatwave risk, coastal flooding risk, and wildfire occurrence risk. For each case, we begin with EDA—summaries, histograms, boxplots, and correlation heatmaps—to build an intuition about the data-generating process. We then fit logistic regression models, generate predicted probabilities, and evaluate performance using confusion matrices, ROC curves, calibration plots, coefficient bar charts, and binned event-rate graphs. By the end, you will have fully runnable R code and a reusable template that you can adapt to real climate datasets, supporting transparent and defensible climate-risk modelling in your own work.

Below is a step-by-step guide (with complete R code) showing how to use logistic regression and basic machine-learning workflows in R to model climate-related binary events.

We’ll build three simulated case studies:

1. Urban Heatwave Risk – probability that a day is classified as a heatwave

2. Coastal Flooding Risk – probability of annual flooding in a low-lying coastal cell

3. Wildfire Occurrence Risk – probability that a region experiences a wildfire in a season

Each case study includes:

• Data simulation

• EDA (exploratory data analysis)

• Logistic regression modelling

• Multiple analytical figures

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