Submarine Landslide Risk Concerning Military Conflicts in the Strait of Hormuz and Gulf of Oman

Environmental Features
This folder contains the raster files of the six environmental variables used as input features for the machine learning models:

Water Depth (Depth.tif)

Slope (Slope.tif)

Roughness (Roughness.tif)

Curvature (Curvature.tif)

Distance to Fault (Fault.tif)

Peak Ground Acceleration (PGA.tif)

All raster files are provided in GeoTIFF format with a spatial resolution of approximately 450 m (15 arc seconds), covering the study area in the Strait of Hormuz and the northern Gulf of Oman.

Model Weights
This folder contains the trained model weights for the four ensemble learning algorithms evaluated in this study:

RandomForest_model.pkl

XGBoost_model.pkl

LightGBM_model.pkl

CatBoost_model.pkl

The models were trained using the environmental features listed above and the global submarine landslide inventory. The weights are saved in Python pickle (.pkl) format and can be loaded using the corresponding libraries (scikit-learn, XGBoost, LightGBM, CatBoost).

Prediction Results
This folder contains the submarine landslide susceptibility prediction results generated by the optimal Random Forest model under eight scenarios:

Background (No Explosion) (Prediction_Background.tif)

Strait of Hormuz – 10 kt Explosion (Prediction_Hormuz_10kt.tif)

Gulf of Oman – 1 t Explosion (Prediction_Oman_1t.tif)

Gulf of Oman – 10 t Explosion (Prediction_Oman_10t.tif)

Gulf of Oman – 100 t Explosion (Prediction_Oman_100t.tif)

Gulf of Oman – 1 kt Explosion (Prediction_Oman_1kt.tif)

Gulf of Oman – 10 kt Explosion (Prediction_Oman_10kt.tif)

Gulf of Oman – 100 kt Explosion (Prediction_Oman_100kt.tif)

Gulf of Oman – 1 Mt Explosion (Prediction_Oman_1Mt.tif)

Each raster file contains the predicted landslide susceptibility probability (ranging from 0 to 1) at a spatial resolution of approximately 450 m. The predictions are provided in GeoTIFF format.