Recursive Feature Elimination for Machine Learning-based Landslide Prediction Models

被引：5
|
作者：
Munasinghe, Kusala [1 ]
Karunanayake, Piyumika [2 ]
机构：
[1] Sri Lanka Technol Campus, Sch Engn & Technol, Padukka, Sri Lanka
[2] Gen Sir John Kotelawala Def Univ, Dept Elect Elect & Telecommun Engn, Ratmalana, Sri Lanka
来源：
3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION (IEEE ICAIIC 2021) | 2021年
关键词：
Landslide prediction; machine learning; recursive feature elimination; SUSCEPTIBILITY;
D O I：
10.1109/ICAIIC51459.2021.9415232
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes a landslide prediction model which uses the recursive feature elimination method. which is one of the key feature selection methods in machine learning that s not tested yet for landslide prediction related applications. The model is tested with the landslide inventories of two landslide-prone areas. The results show that the proposed model achieves an average accuracy of 91.15% and a sensitivity of 83.4% predicting the possibility for a landslide. The findings of this research paper imply that recursive feature elimination can also he effective') used in landslide predictions since it achieves high accuracy.
引用
收藏
页码：126 / 129
页数：4
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