Predicting London’s precipitation: a spatio-temporal neural network approach

This study presents a data-driven approach to forecasting total precipitation in London using an Artificial Neural Network (ANN) within a spatio-temporal framework. Leveraging ERA5 data from 2010 to 2025, the methodology includes automated NetCDF extraction, feature engineering with lagged precipitation and cyclic time encodings, and dimensionality reduction via a trained Autoencoder. The ANN, designed in a GenCast-style architecture, was trained using the Adam optimiser over 50 epochs and achieved strong performance. SHAP analysis highlighted the importance of lag features and seasonal time variables, enhancing interpretability and supporting the model’s application in urban flood risk management and climate resilience.

This version of the paper has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-ma...), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-032-11442-6_13