Journal of Engineering and Applied Sciences
Year:
2018
Volume:
13
Issue:
1 SI
Page No.
2301 - 2308
References
Brownlee, J., 2016. Get your data ready for machine learning in R with pre-processing. Machine Learning Mastery, Toronto, Ontario, Canada. http://machinelearningmastery.com/pre-process-your-dataset-in-r/
Corominas, J., J. Moya, A. Ledesma, A. Lloret and J.A. Gili, 2005. Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain). Landslides, 2: 83-96.
CrossRef | Direct Link | Famili, A., W.M. Shen, R. Weber and E. Simoudis, 1997. Data preprocessing and intelligent data analysis. Intell. Data Anal., 1: 3-23.
Direct Link | Kipnis, I., 2017. Testing the hierarchical risk parity algorithm. R bloggers, Oakland California. https://www.r-bloggers.com/
Lee, S. and B. Pradhan, 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4: 33-41.
CrossRef | Lee, S. and T. Sambath, 2006. Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ. Geol., 50: 847-855.
CrossRef | NASA., 2017. Global landslide catalog export. NASA's Open Data Portal, USA. https://data.nasa.gov/dataset/Global-Landslide-Catalog-Export/dd9e-wu2v?category=dataset&%20view%20name=Global-Landslide-Catalog-Export
Pal, M., 2003. Random forests for land cover classification. Proceedings of the 2003 IEEE International Symposium on Geoscience and Remote Sensing (IGARSS '03), July 21-25, 2003, IEEE, Kurukshetra, India, ISBN:0-7803-7929-2, pp: 3510-3512.
Rodriguez, G.V.F., M.C. Olmo, F.A. Hernandez, P.M. Atkinson and C. Jeganathan, 2012. Random forest classification of mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens. Environ., 121: 93-107.
Direct Link | Yeo, I.K. and R.A. Johnson, 2000. A new family of power transformations to improve normality or symmetry. Biometrika, 87: 954-959.
CrossRef |