Home ›
Entities
› academia
› Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms
Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms
Research article (Environmental Science and Pollution Research, 2021) · cited 47× · AI/ML
Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms
Summary
Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms is a scholarly article[1].
Key Facts
Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms. Retrieved May 24, 2026, from https://4ort.xyz/entity/demystifying-uncertainty-in-pm10-susceptibility-mapping-using-variable-drop-off-in-extreme-gradient-boosting-xgb-and-ran
MLA“Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/demystifying-uncertainty-in-pm10-susceptibility-mapping-using-variable-drop-off-in-extreme-gradient-boosting-xgb-and-ran.
BibTeX@misc{4ortxyz_demystifying-uncertainty-in-pm10-susceptibility-mapping-using-variable-drop-off-in-extreme-gradient-boosting-xgb-and-ran_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms}}, year = {2026}, url = {https://4ort.xyz/entity/demystifying-uncertainty-in-pm10-susceptibility-mapping-using-variable-drop-off-in-extreme-gradient-boosting-xgb-and-ran}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms — https://4ort.xyz/entity/demystifying-uncertainty-in-pm10-susceptibility-mapping-using-variable-drop-off-in-extreme-gradient-boosting-xgb-and-ran (retrieved 2026-05-24)
