A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network

By Liangjie Wang, Min Guo, Kazuhide Sawada, Jie Lin, Jinchi Zhang. Geoscience journal, 2016

202 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/cb6c4a3fde9f40b21ba618708ccf90469c606056

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Biswajeet Pradhan

In Global and Planetary Change

1. Seyed Amir Naghibi
2. Bahareh Kalantar
3. A. Motevalli
4. Shattri Mansor
5. Biswajeet Pradhan
6. Hamid Reza Pourghasemi
7. Binh Thai Pham
8. D. Tien Bui

In Management, Monitoring, Policy and Law

1. Seyed Amir Naghibi
2. Bahareh Kalantar
3. A. Motevalli
4. Shattri Mansor
5. Biswajeet Pradhan
6. D. Tien Bui
7. Hamid Reza Pourghasemi
8. Binh Thai Pham

Citations in the corpus

• Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) (2018, methodology): “Even then, Wang et al. (2016) found that the LR model outperformed the ANN model, and selecting appropriate landslide samples for training the models can improve model accuracy.”