Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran
By Ruhollah Taghizadeh‐Mehrjardi, Kamal Nabiollahi, R. Kerry. 2016
247 citations recorded by the source corpus.
Subfields: Environmental Engineering, Management, Monitoring, Policy and Law, Soil Science
View Paper: https://www.semanticscholar.org/paper/b2646a2201abeddd650133926086055fb9728f63
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
- Budiman Minasny
- Alex B. McBratney
- José Padarian
- Yones Khaledian
- Bradley A. Miller
- Sabine Grunwald
- Tomislav Hengl
- R. M. Lark
In Management, Monitoring, Policy and Law
- Alex B. McBratney
- Budiman Minasny
- José Padarian
- Bradley A. Miller
- Yones Khaledian
- Sabine Grunwald
- Tomislav Hengl
- R. M. Lark
In Soil Science
- Alex B. McBratney
- Budiman Minasny
- José Padarian
- Bradley A. Miller
- Yones Khaledian
- Tomislav Hengl
- Alfred E. Hartemink
Citations in the corpus
- Machine learning and soil sciences: a review aided by machine learning tools (2019, background): “For instance, RF is mostly considered in the category of low interpretability (Brungard et al., 2015; Were et al., 2015; Taghizadeh-Mehrjardi et al., 2016; Deng et al., 2018), but its use is also sometimes justified due to its ease of interpretability via the use of variables of importance (Jeong…”
- Selecting appropriate machine learning methods for digital soil mapping (2020, methodology): “[30] SOC (0-15cm) (g kg -1 ) 835 Str DTA+C 0.”