Can I read Feature Elimination and Comparison of Machine Learning Algorithms in Landslide Susceptibility Mapping on EtoBox?

Feature Elimination and Comparison of Machine Learning Algorithms in Landslide Susceptibility Mapping by Jesudasan Jacinth Jennifer is a Environmental Science article available to read on EtoBox.

What is Feature Elimination and Comparison of Machine Learning Algorithms in Landslide Susceptibility Mapping about?

Landslide susceptibility assessment was adopted for the Idukki region using 6 machine learning models viz., Adaptive Boosting (AdaBoost), Naïve Bayes (NB), Neural Network (NNET), Random Forest (RF), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) models. Due to the severe calamity caused by landslides in the Idukki district during the August 2018 monsoon, the district was listed as one of the top hotspots for landslides, which is why this study focuses on them. Eighteen conditioning factors that influenced the occurrence of the landslides in the Idukki region were considered primarily in this study. However, the Recursive Feature Elimination (RFE) approach was adopted to eliminate the variables with low importance. The research focuses on the sensitivity of the causative factors and explores the conditioning parameters that are considered necessary by each machine learning algorithm. Furthermore, the Accuracy, Kappa index, ROC-AUC, Sensitivity, Specificity were computed to estimate the performance of the models. The RF model portrayed the best prediction accuracy in training and testing cases, followed by the AdaBoost and XGBoost models. However, while determin

Who reads Feature Elimination and Comparison of Machine Learning Algorithms in Landslide Susceptibility Mapping?

Author

Jesudasan Jacinth Jennifer

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Environmental Science (Physical Sciences)