Application of machine learning methods to spatial interpolation of environmental variables

By Jin Li, Andrew D. Heap, Anna Potter, James Daniell. Environmental Modelling & Software, 2011

348 citations recorded by the source corpus.

Subfields: Environmental Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/5abc1b4071c5d48f90d1276d62abfed1b9657117

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. Hanna Meyer
2. Jin Li
3. Andrew D. Heap
4. Stephan Wöllauer
5. Thomas Nauss
6. Christoph Reudenbach
7. Edzer J. Pebesma
8. Budiman Minasny

In Soil Science

1. Jin Li
2. Andrew D. Heap

Citations in the corpus

• Spatial interpolation methods applied in the environmental sciences: A review (2014, methodology): “These methods include: 1) support vector machine (SVM) (Gilardi, 2002; Li et al., 2011c, 2010), 2) random forest (RF) (Bustamante et al., 2011; Li et al., 2011a, 2011b, 2012), 3) neural network (Kanevski et al., 2008; Li et al., 2010; Lin and Chen, 2004), 4) neuro-fuzzy network (Friedel and…”
• Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction (2019, background): “Spatial coordinates are used especially often (Li et al., 2011; Langella et al., 2010; Shi et al., 2015; Janatian et al., 2017; Walsh et al., 2017; Jing et al., 2016; Wang et al., 2017; Georganos et al., 2019).”