What does this research mean for the field?

An integrated approach using Principal Component Analysis to resolve multicollinearity combined with a Permutation-Weighted machine learning model significantly improves the predictive accuracy of landslide susceptibility mapping. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

November 16, 2025Applied SciencesOpen Access

Landslide Susceptibility Mapping Optimization for Improved Risk Assessment Using Multicollinearity Analysis and Machine Learning Technique

View Full Paper

Ask AI

Bookmark

Authors

BJBuddhi Raj Joshi NBNetra Prakash Bhandary IAIndra Prasad Acharya

Discussion

Member takes

Overview

Key Points

Key points are not available for this paper at this time.

Cite This Study

Joshi et al. (2025) studied this question.

synapsesocial.com/papers/6a24300798edd3e1959efba9 https://doi.org/10.3390/app152212152

View Full Paper

Ask AI

Bookmark

November 16, 2025Applied SciencesOpen Access

Landslide Susceptibility Mapping Optimization for Improved Risk Assessment Using Multicollinearity Analysis and Machine Learning Technique

View Full Paper

Ask AI

Bookmark

Authors

BJBuddhi Raj Joshi NBNetra Prakash Bhandary IAIndra Prasad Acharya

Discussion

Member takes

Overview

Key Points

Key points are not available for this paper at this time.

Cite This Study

Joshi et al. (2025) studied this question.

synapsesocial.com/papers/6a24300798edd3e1959efba9 https://doi.org/10.3390/app152212152

Also Consider

Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark