Spatial Decision Tree Learning
Spatial Decision Tree Learning is a machine learning method that extends conventional decision tree induction by incorporating spatial autocorrelation and spatial heterogeneity into the splitting criteria to improve predictive accuracy in geographically referenced data.
34
Publications
2.9K
Citations
86
Authors
54
Institutions
Publications per year
2017–2026
14
Parents
34
Mahyat Shafapour Tehrany, Biswajeet Pradhan, Mustafa Neamah Jebur · Journal of Hydrology · 2013 · 840 citations
Computing, Geoinformatics, Hydrology +17
Binh Thai Pham, Dieu Tien Bui, M. B. Dholakia et al. · Geotechnical and Geological Engineering · 2016 · 159 citations
Computing, Hydrology, Meteorology +17
1–5 of 34
86
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3
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1–5 of 86
54
IPB University
Bogor, Indonesia
5 h-index
7 publications
151 citations
Universiti Putra Malaysia
Seri Kembangan, Malaysia
3 h-index
7 publications
2.6K citations
Government of Gujarat
Gandhinagar, India
2 h-index
2 publications
268 citations
North Dakota State University
Fargo, United States
2 h-index
6 publications
225 citations
University of Technology Sydney
Sydney, Australia
2 h-index
2 publications
214 citations

Minnesota Department of Natural Resources
Saint Paul, United States
2 h-index
4 publications
162 citations

Sejong University
Seoul, South Korea
2 h-index
2 publications
214 citations
University Of Transport Technology
Hanoi, Vietnam
2 h-index
2 publications
268 citations
1–8 of 54
2 publications · 333 citations
2
h-index
1 publications · 26 citations
1
h-index
1 publications · 74 citations
1
h-index
1 publications · 840 citations
1
h-index
1 publications · 11 citations
1
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1–5