St. Lawrence Lowlands Precipitation Data: 30-Year Trends Prediction

In this phase of the analysis, we aim to model precipitation patterns in the St. Lawrence Lowlands using machine learning techniques, leveraging historical climate and environmental data. We will compare Random Forest, XGBoost, and Mars models to assess their ability to capture complex relationships and predict precipitation trends. Model performance will be evaluated using cross-validation and regression metrics to determine the most effective approach.

St. Lawrence Lowlands Precipitation Data: 30-Year Trends & Anomalies

Understanding long-term precipitation patterns is essential for climate research, agriculture, and water resource management. In this post, we analyze 30 years of precipitation data from the AgERA5 dataset for St. Lawrence Lowlands, using exploratory data analysis (EDA) techniques to uncover trends, seasonal variations, and anomalies.

TyT2024W21 - VIZ:Carbon Majors Emissions Data

This week we are exploring historical emissions data from Carbon Majors. They have complied a database of emissions data going back to 1854. In the first and second part I did some EDA and created a spatio-temporal machine learning model. In this part, I’m creating an animated vizualisation of the data including the prediction.

TyT2024W21 - ML:Carbon Majors Emissions Data

This week we’re exploring historical emissions data from Carbon Majors. They have complied a database of emissions data going back to 1854. In this second part, I’m predicting carbon emission over space and time.

TyT2024W21 - EDA:Carbon Majors Emissions Data

This week we are exploring historical emissions data from Carbon Majors. They have complied a database of emissions data going back to 1854. In this first part, I start with some exploratory data analysis.