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

Dataset DOI: [10.5061/dryad.wh70rxx20](10.5061/dryad.wh70rxx20)

**Description of the data and file structure**

This data is for the Nile Delta shoreline. The satellite images were collected from the USGS LANDSAT database. Wave direction, wave period, and swell height were obtained from the ERA5 reanalysis archive, which provides historical climate information at a monthly resolution across multiple. Land cover data from 1992 to 2019 were derived from the ESA Climate Change Initiative and were extended to the year 2050 using projected layers from ArcGIS Living Atlas. Sea level rise and land subsidence data were sourced from the CMIP6 ensemble projections, the IPCC Sixth Assessment Report.

**Files and variables**

**File: datalong_cumu_NSM_new_inc_ang_latlong_no2022.csv**

**Description:** Model Training Variables

**Variables**

| **Feature** | <br /> | <br /> ...