A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping

By Seyed Amir Naghibi, D. D. Moghaddam, Bahareh Kalantar, Biswajeet Pradhan, Ozgur Kisi. 2017

204 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/b0c4858b4054f707fd76ef7f1e29ae376305fb0e

How researchers see this paper

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

In Environmental Engineering

1. Biswajeet Pradhan
2. Seyed Amir Naghibi
3. Hamid Reza Pourghasemi

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

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): “One SVM model drawback is the considerable time required for application (Naghibi et al. 2017), while LR requires lesser time.”