├── Landslide Data.xls ├── Report_Group_26.pdf └── README.md /Landslide Data.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ryanbob96/Landslide-Prediction-2008-Wenchuan-Earthquake-Sichuan-China/HEAD/Landslide Data.xls -------------------------------------------------------------------------------- /Report_Group_26.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ryanbob96/Landslide-Prediction-2008-Wenchuan-Earthquake-Sichuan-China/HEAD/Report_Group_26.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Landslide-Prediction-2008-Wenchuan-Earthquake-Sichuan-China 2 | 3 | Abstract.The objective of machine learning study for landslides are to predict the occurrence of landslide 4 | events as well as to identify several parameters which influence the landslide susceptibility. The study 5 | area is in the Northern End of the Longmenshan Fault, Sichuan Province, China. In this study, the authors 6 | used both the IBM SPSS program and the python language program to analyze and model the data 7 | provided. In data preparation, several steps are performed including data cleaning, aspect 8 | transformation, lithology separation, factor of safety (fos) classification, transformation, and 9 | normalization. To test the model accuracy, data partition was applied. The main modeling technique 10 | performed with SPSS software including Decision Tree, Support Vector Machine (SVM), Neural Network 11 | and KNN with additional logistic regression, K-Means, and PCA which run in python. Models are 12 | evaluated by using the confusion matrix with precision, recall, f1 score and accuracy to help determine 13 | the most suitable model. In general, all models with an accuracy score of more than 0.8, could be used 14 | to analyzed landslide susceptibility. Based on the analysis of data provided; scarps distance, specific 15 | weight, and lithology of moraine play the major roles for landslide susceptibility. 16 | Keywords: Machine Learning, Landslide Susceptibility, Confusion Matrix, Scarps Distance 17 | --------------------------------------------------------------------------------