000			01718nab a22001577a 4500
999			_c83734 _d83734
005			20230117084611.0
008			230117b ||||| |||| 00| 0 eng d
100	1		_aVictor, Jaime Angelo S. _eauthor
245	1	0	_aMultivariate logistic regression approach for landslide susceptibility assessment of Antipolo, Rizal
264		4	_c2018
520	3		_aSlope instability associated with heavy rainfall or earthquake is a familiar geotechnical problem in the Philippines. This study aims to perform a detailed landslide susceptibility assessment of Antipolo City using a statistical approach. In this study, morphologic and non-morphologic factors contributing to landslide occurrence and their corresponding spatial relationships were considered. The multivariate logic regression was performed in randomly selected datasets based on the landslide inventory. These were divided into training and test data sets based on K-cross fold validation scheme resulting to different models. The model selected for the final implementation has an overall accuracy of 91.66%, AUROC of 0.908, standard error of 0.002 and RMSE of 0.2478. Cross validation with deterministic approach using physically based slope stability models were performed, where there was no significant difference between the two approaches in identifying areas of highly and very highly susceptible to landslide occurrence. The study also shows that almost 40% of Antipolo City has been assessed to be potentially dangerous areas in terms of landslide occurrence.
650		0	_aMultivariate analysis
700	1		_aZarco, Mark Albert H. _eauthor
773			_tPhilippine Engineering Journal _gvol. 39, no. 2: (December 2018), pages 27-40
942			_2ddc _cART