Last modified: November 2024.
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 Fang et al. (2023) (https://doi.org/10.1016/j.jag.2023.103631), 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 inputs can be accessed here, as well as on Zenodo
Recommended citation: Haque, DME et al. (2024). Data and code for Decoding dynamic landslide hazard processes for a massive refugee camp (KTP) in Bangladesh (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14184223
I:
| Type | Code | Input |
|---|---|---|
| R Script | GAM_Pyrates | Geopackage Files |
| R Script | SizeFit_Oct24 | Theend17082023 - Copy.txt |
| R Script | ML_Pyrates | Theend17082023csv.csv |
II:
| Type | Code | Input |
|---|---|---|
| R Markdown | GAM_LSA | Geopackage Files |
| R Markdown | ML_LSA | Theend17082023csv.csv |
III:
| Type | Code | Input |
|---|---|---|
| Python Script | Landslide_MonteCarlo.ipynb | .xls file |
The code is distributed "as is" WITH NO WARRANTY whatsoever!