Training dataset for Nile delta shoreline change prediction until 2050 using machine learning

This dataset contains shoreline position and environmental forcing data
used to train, validate, and apply a shallow artificial neural network to
forecast shoreline change in the Nile Delta. Shoreline positions were
digitized from Landsat imagery (1992–2017) at 125-meter transect spacing
across six geomorphologically distinct shoreline segments. Environmental
variables include wave direction, wave period, swell height (ERA5), sea
level rise (CMIP6/IPCC AR6), land subsidence, and land cover change (ESA
CCI, ArcGIS Living Atlas). The 1992–2017 dataset was used for model
training, the 2022 data for independent validation, and the trained model
was applied to generate forecasts for 2030, 2040, and 2050 shoreline
positions. Feature selection using Spearman correlation and permutation
importance identified the most influential predictors. This dataset
supports the reproduction of results reported in Forecasting Nile Delta
Shoreline Change Until 2050 Using a Shallow Neural Network and applies to
other coastal forecasting and risk assessment studies.