12 citations as recorded by crossref.

1. Habitat image annotation with low-level features, medium-level knowledge and location information M. Torres & G. Qiu https://doi.org/10.1007/s00530-014-0445-2
2. Uncertainty propagation in vegetation distribution models based on ensemble classifiers J. Peters et al. https://doi.org/10.1016/j.ecolmodel.2008.12.022
3. Automatic habitat classification using image analysis and random forest M. Torres & G. Qiu https://doi.org/10.1016/j.ecoinf.2013.08.002
4. Impacts of groundwater and climate variability on terrestrial groundwater dependent ecosystems: a review of geospatial assessment approaches and challenges and possible future research directions C. Chiloane et al. https://doi.org/10.1080/10106049.2021.1948108
5. Land use and climate control the spatial distribution of soil types in the grasslands of Inner Mongolia F. Barthold et al. https://doi.org/10.1016/j.jaridenv.2012.08.004
6. The Impacts of Groundwater Chemistry on Wetland Vegetation Distribution in the Northern Qinghai–Tibet Plateau J. Zhou et al. https://doi.org/10.3390/su11185022
7. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources H. Tyralis et al. https://doi.org/10.3390/w11050910
8. Effects and significance of groundwater for vegetation: A systematic review K. Glanville et al. https://doi.org/10.1016/j.scitotenv.2023.162577
9. Critical role of vegetation and human activity indicators in the prediction of shallow groundwater quality distribution in Jianghan Plain with LightGBM algorithm and SHAP analysis H. Xiong et al. https://doi.org/10.1016/j.chemosphere.2025.144278
10. Wetland vegetation distribution modelling for the identification of constraining environmental variables J. Peters et al. https://doi.org/10.1007/s10980-008-9261-4
11. Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification P. Baudron et al. https://doi.org/10.1016/j.jhydrol.2013.07.009
12. A Medium and Long-Term Runoff Forecast Method Based on Massive Meteorological Data and Machine Learning Algorithms Y. Li et al. https://doi.org/10.3390/w13091308