DOI: 10.5061/dryad.wh70rxx20
title: Training dataset for Nile delta shoreline change prediction until 2050 using machine learning

description: 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.
description: # 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** | 
 
 | 
 
 | | :-------------- | :---------- |
 :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 
 
 | **Unit** | **Description** | | TransID | meters | Represents the unique
 spatial identifier multiplied by transect spacing (125 m) for each
 transect at each fixed location from the start point at the west of the
 delta along the shoreline at different times (e.g., TransID 5 means 5\*125
 meters from the start point at East Alexandria) | | Segment | unitless | A
 categorical variable representing one of the six shoreline segments,
 providing geographical information for similarly correlated transects. | |
 Period | Year | This is the end year of any specific time interval for
 each set of data, representing the temporal changes over the same
 transects. | | Azimuth | degrees | Represents the angular measurement of
 the shoreline's orientation from north at each transect. | |
 epr\_corr | unitless | EPR Correlations are values indicating the
 correlation between adjacent transects to highlight spatial dependencies,
 where high correlation values mean similar adjacent EPRs and low
 correlation values mean an abrupt change or switch in the EPR. | | wv\_dir
 | degrees | Wave Direction represents the mean direction of ocean surface
 waves, accounting for both wind-sea and swell waves given in degrees true,
 indicating where the waves are coming from (e.g., 0 degrees from the
 north, 90 degrees from the east) at each transect, which can relate to
 longshore transport. | | wv\_per | seconds | Wave Period represents the
 time between two successive waves for both locally generated wind-sea
 waves and distant swell waves, which can influence the number of sediment
 transport events. | | sw\_height | meters | Swell Height represents the
 average height of surface ocean waves, including both wind-sea waves and
 swell waves, collected every other month in each given year. Wind-sea
 waves are directly influenced by local winds, whereas swell waves are
 generated by winds at distant locations and times. It indicates the
 vertical distance between wave crests and troughs. | | sl\_rise |
 millimeters | Sea level rise difference value at each year and location
 relative to the 1992 sea level. | | subsidence | millimeters | Subsidence
 difference value at each year and location relative to the 1992 sea level.
 | | cropland | km^2^ | Cropland area change within the delta. | | nat\_veg
 | km^2^ | Natural vegetation area change within the delta. | | urban |
 km^2^ | Urban area change within the delta. | | bare | km^2^ | Bare area
 change within the delta. | | lat | degrees | Latitude value of the data
 point at the shoreline. | | long | degrees | Longitude value of the data
 point at the shoreline. | | cumulative\_NSM | meters | The cumulative net
 value change in shoreline position in reference to the 1992 shoreline. |
 ## **Code/software** Google Sheets can open
 "datalong_cumu_NSM_new_inc_ang_latlong_no2022.csv" as a csv
 file. ## **Access information** Other publicly accessible locations of the
 data: • USGS Landsat database\ • IPCC Sixth Assessment Report\ • CMIP6
 ensemble projections\ • ESA ERA5 reanalysis archive Data was derived from
 the following sources: * USGS Landsat database, IPCC Sixth Assessment
 Report, CMIP6 ensemble projections, ESA ERA5 reanalysis archive.