A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Models
2.2.1. Geographical Random Forest
GRF Computational Efficiency
GRF Bandwidth Optimization
Spatial Weighting
2.2.2. Benchmark Models and Validation Metrics
3. Results
3.1. GRF and GWR Bandwidth Optimization
3.2. Predictive Performance
3.3. Computational Improvements
4. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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OLS | RF | GRF | GRF-W | GWR | |
---|---|---|---|---|---|
RMSE | 5024.7 | 4104.6 | 3071.2 | 2801.5 | 3259.00 |
MAE | 3949.6 | 2578.7 | 1763.2 | 1580.4 | 1933.6 |
R2 | 0.45 | 0.61 | 0.79 | 0.82 | 0.77 |
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Georganos, S.; Kalogirou, S. A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests. ISPRS Int. J. Geo-Inf. 2022, 11, 471. https://doi.org/10.3390/ijgi11090471
Georganos S, Kalogirou S. A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests. ISPRS International Journal of Geo-Information. 2022; 11(9):471. https://doi.org/10.3390/ijgi11090471
Chicago/Turabian StyleGeorganos, Stefanos, and Stamatis Kalogirou. 2022. "A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests" ISPRS International Journal of Geo-Information 11, no. 9: 471. https://doi.org/10.3390/ijgi11090471