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Machine-learning workflow for satellite-derived bathymetry in Lake Burullus and Lake Manzala

Domaine:

geospatialenvironment and energy

Type de record:

software
Créateur:
ShaMohElaFay
Éditeur:
Zenodo
Hôte:avatar
This software archive supports the study “Machine Learning–Based Satellite-Derived Bathymetry in Optically Complex Coastal Lagoons: Model Performance, Transferability, and Explainability.” It contains the custom Python workflow used for spectral-predictor generation, predictor screening, stratified 70/30 data partitioning, Z-score standardization, five-fold cross-validation, final model fitting, independent validation, spatial residual export, SHAP interpretation, and longitudinal bathymetric-profile visualization for Lake Burullus and Lake Manzala, Egypt. The workflow includes a Multiple Linear Regression baseline and five machine-learning algorithms: Random Forest, XGBoost, CatBoost, Support Vector Regression, and Multilayer Perceptron. Lagoon-specific configurations, environment specifications, documentation, and input-data templates are included. Original field bathymetric observations and processed Sentinel-2 calibration datasets are not redistributed in this software record and are governed by the Data Availability statement of the associated article.

Visit

doi.org

Tags

satellite-derived bathymetrySentinel-2machine learningcoastal lagoonsLake BurullusLake ManzalaSHAPbathymetric mappingremote sensingEgypt

Licenses

MIT Licensehttps://opensource.org/licenses/MIT

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