Hyperlocal Weather Forecasting Using Multimodal Data Fusion with Explainable AI

A hyperlocal weather forecasting system that fuses multimodal data and explains its predictions.

A hyperlocal weather forecasting system that fuses multimodal data and explains its predictions. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are explanations of each result (feature importance or visual evidence), fusion of more than one type of input, data preparation and exploration, model training and comparison of algorithms, and evaluation with standard metrics. The reference build uses SHAP, Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.