Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping

By Seyed Amir Naghibi, K. Ahmadi, A. Daneshi. Water resources management, 2017

430 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/3df7f7dbda3cbc1c30e35d35ab373d4c2e814098

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. Haoyuan Hong
2. Wei Chen
3. Baharin bin Ahmad
4. Himan Shahabi
5. Ozgur Kisi
6. Jan Adamowski
7. Kwok-Wing Chau
8. Masoud Karbasi

In Global and Planetary Change

1. Ataollah Shirzadi
2. Himan Shahabi
3. Baharin bin Ahmad
4. Haoyuan Hong
5. Wei Chen
6. J. Hui
7. Manna Xi
8. Chen Guo

In Water Science and Technology

1. Himan Shahabi
2. Baharin bin Ahmad
3. Ozgur Kisi
4. Masoud Karbasi
5. Jan Adamowski
6. Ravinesh C. Deo
7. Kwok-Wing Chau
8. Mohammad Zounemat‐Kermani

Citations in the corpus

• Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms (2021, methodology): “In groundwater-potential mapping, the machinelearning models used so far are classified as random forest (RF) [27], support vector machine (SVM) [28], boosted regression trees (BRT) [29], classification and regression trees (CART) [30], linear discriminant analysis (LDA) [31], multivariate adaptive ”
• A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment (2018, background): “These models are also widely used in groundwater potential mapping and other fields of study (Naghibi et al. 2017).”