Data and code for Decoding dynamic landslide hazard processes for a massive refugee camp (KTP) in Bangladesh

The codes have been implemented using R 4.4.0. Landslide priority zonation using Monte Carlo simulation is implemented in Google Colab. A Dynamic Landslide Hazard Assessment has been conducted using a Generalized Additive Model (GAM). The results of the GAM are also compared with standard machine learning algorithms (MLs): NNET, RF, LDA, xgBoost, and SVM. The code is jointly developed by Dewan Haque and Ritu Roy, with collaboration from many others. The GAM code is an update from the study published by Zhice, F. (2023), https://doi.org/10.5281/zenodo.10395153, adapted to apply it across settings. The ML code has been developed from scratch. The required data from intensive fieldwork and satellite image analysis is uploaded here to reproduce the results. Additionally, R Markdown files are provided. The ReadMe file here, as well as on GitHub, will be useful for further instructions. GitHub Link: https://github.com/Dewan-cpu/Decoding-Landslide-Hazard-Assessment