An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping

By Brandon Heung, H. Ho, Jin Zhang, Anders Knudby, Chuck E. Bulmer, Margaret G. Schmidt. 2016

442 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/69081d1390e89d97f7812bcdd929ea66e92bb045

How researchers see this paper

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

In Artificial Intelligence

1. Alex B. McBratney
2. Budiman Minasny
3. Alexandre M. J.-C. Wadoux
4. Sabine Grunwald

In Civil and Structural Engineering

1. Alex B. McBratney
2. Budiman Minasny
3. Alexandre M. J.-C. Wadoux
4. Attila Nemes
5. Walter J. Rawls
6. Tomislav Hengl
7. Yakov A. Pachepsky
8. Alfred E. Hartemink

In Environmental Engineering

1. Budiman Minasny
2. Alex B. McBratney
3. Alexandre M. J.-C. Wadoux
4. Sabine Grunwald
5. Tomislav Hengl
6. R. M. Lark
7. Jasper A. Vrugt
8. Oliver Watts

Citations in the corpus

• Machine learning for digital soil mapping: Applications, challenges and suggested solutions (2020, methodology): “About 80% of the case studies used at least one tree-based algorithm such as regression tree (e.g. Taghizadeh-Mehrjardi et al., 2019b; Heung et al., 2016), RF (e.g. Häring et al., 2012) or boosted regression tree (e.g. Lorenzetti et al., 2015).”