Soil type classification and estimation of soil properties using support vector machines
By Milos Kovacevic, Branislav Bajat, B. Gajić. 2010
194 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Environmental Engineering, Management, Monitoring, Policy and Law
View Paper: https://www.semanticscholar.org/paper/14d7ab96156babec2f6b59a69d88eb0cfe81bba3
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
- THOMAS C. EDWARDS JR.
- A. Douaik
- J. Boettinger
- Monther Alhamdoosh
- Bradley A. Miller
- Wim Cornelis
- Yones Khaledian
- Michael C. Duniway
In Environmental Engineering
- Donald Gabriëls
- Wim Cornelis
- Muhammed Khlosi
- A. Douaik
- Monther Alhamdoosh
- THOMAS C. EDWARDS JR.
- J. Boettinger
- Bradley A. Miller
In Management, Monitoring, Policy and Law
Citations in the corpus
- Selecting appropriate machine learning methods for digital soil mapping (2020, methodology): “Support vector machine/regression (SVM/SVR) is a ML algorithm that has recently gained some popularity in digital soil mapping [32], [33], [34], [35].”
- Enhanced pedotransfer functions with support vector machines to predict water retention of calcareous soil (2016, methodology): “Support vector regression is an attractive machine-learning method that has been successfully applied to predict soil properties (e.g. Lamorski et al., 2008; Twarakavi et al., 2009; Kovačević et al., 2010; Haghverdi et al., 2014).”
- Machine learning for predicting soil classes in three semi-arid landscapes (2015, background): “…2003; Kim et al., 2012; Scull et al., 2005), 77 random forests (Barthold et al., 2013; Pahlavan Rad et al., 2014; Poggio et al., 2013; Stum et al., 2010), 78 neural networks (Behrens et al., 2005; Jafari et al., 2013; Moonjun et al., 2010), and support vector 79 machines (Kovačević et al., 2010).”