当前位置：
X-MOL 学术
›
Prog. Phys. Geogr.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Assessing the effectiveness of alternative landslide partitioning in machine learning methods for landslide prediction in the complex Himalayan terrain
Progress in Physical Geography: Earth and Environment ( IF 3.9 ) Pub Date : 2022-07-11 , DOI: 10.1177/03091333221113660 Muhammad Tayyib Riaz 1 , Muhammad Basharat 1 , Maria Teresa Brunetti 2
Progress in Physical Geography: Earth and Environment ( IF 3.9 ) Pub Date : 2022-07-11 , DOI: 10.1177/03091333221113660 Muhammad Tayyib Riaz 1 , Muhammad Basharat 1 , Maria Teresa Brunetti 2
Affiliation
Several devastating landslides have occurred in the NW Himalayas, which has prompted several researchers to strive for improvement in landslide susceptibility modelling (LSM) methodologies. This re...
中文翻译：
在复杂的喜马拉雅地形滑坡预测机器学习方法中评估替代滑坡分区的有效性
西北喜马拉雅山发生了几起毁灭性的滑坡，这促使一些研究人员努力改进滑坡敏感性建模 (LSM) 方法。这个再...
更新日期：2022-07-11
中文翻译：
在复杂的喜马拉雅地形滑坡预测机器学习方法中评估替代滑坡分区的有效性
西北喜马拉雅山发生了几起毁灭性的滑坡，这促使一些研究人员努力改进滑坡敏感性建模 (LSM) 方法。这个再...
京公网安备 11010802027423号