LandslideML

About

'LandslideML' is a Python package designed to generate landslide susceptibility map using machine learning models present in the scikit-learn toolbox. It is capable of:

• Creating a Classifier Machine Learning Model amongst tested types (Random Forest, SVM or GBM)
• Training and validating such models with scikit-learn
• Predicting the label values for a given dataset and mapping to a dataframe
• Plotting the resulting structure into a 2D map

Features

• Read and preprocess datasets for landslide susceptibility analysis.
• Train multiple machine learning models including SVM, Logistic Regression, K-Nearest Neighbors, Decision Trees, Gradient Boosting, and Neural Networks.
• Generate and save susceptibility maps.

Installation

Dependencies

Ensure you have the required dependencies installed, which are listed in requirements.txt. You can install them using:

pip install -r requirements.txt

User installation

To install the library, you can use the 'pip' command:

pip install landslideml

Folder structure

landslideml ├─── README.md ├─── LICENSE ├─── NOTES.md ├─── examples │ ├─── workflow_comparison_models.py │ ├─── workflow_gbm.py │ ├─── workflow_random_forest.py │ └─── workflow_svm.py ├─── landslideml │ ├─── init.py │ ├─── config.py │ ├─── model.py │ ├─── output.py │ └─── reader.py ├─── pyproject.toml ├─── requirements.txt ├─── setup.py ├─── testcase_data │ ├─── prediction.nc │ ├─── sample_prediction.nc │ ├─── shapefile.shp │ ├─── shapefile.shx │ └─── training.csv ├─── tests │ ├─── test_gbm_workflow.py │ ├─── test_model_evaluate_model.py │ ├─── test_model_mapping.py │ ├─── test_model_predict.py │ ├─── test_model_save_model.py │ ├─── test_model_setup.py │ ├─── test_output_compare_features.py │ ├─── test_output_heatmap.py │ ├─── test_output_plot_map.py │ ├─── test_reader_generate_model.py │ ├─── test_reader_load_model.py │ └─── test_svm_workflow.py

Examples

Some usage examples of the library can be found in the examples folder

License

This software is distributed under the MIT License and further information about the license can be found in the LICENSE file

Third-party libraries

The library currently uses the following third-party libraries:

• scikit-learn
• pandas
• numpy
• matplotlib
• xarray
• joblib
• seaborn
• netcdf4

Contributing

The project is in the early development phase. Upon completion and delivery, contributions will be welcome! Please fork the repository and submit a pull request for any improvements or bug fixes.