On the public citation graph

Spatial heterogeneity of anthropogenic effects on winter wildfire risk in South Korea: A SHAP-based XGBoost analysis with spatial block cross-validation

https://doi.org/10.2139/ssrn.7477812
0 signed · 14 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/ecm.1557openalex-snapshot
Cross validation for model selection: A review with examples from ecology
inferredcites →https://doi.org/10.1002/ppul.71489openalex-snapshot
Air Pollution and Pulmonary Health in American Indian/Alaska Native Children: Describing and Addressing Inequitable Environmental Exposures
inferredcites →https://doi.org/10.1007/s11069-025-07169-4openalex-snapshot
Spatial and temporal variability of forest fires in the Republic of Korea over 1991–2020
inferredcites →https://doi.org/10.1007/s11146-022-09915-yopenalex-snapshot
Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach
inferredcites →https://doi.org/10.1007/s41748-025-00863-0openalex-snapshot
Predicting Forest Fire Risk and Carbon Pool Vulnerability in the Himalayas: A Machine Learning Approach
inferredcites →https://doi.org/10.1029/2022wr032789openalex-snapshot
Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting
inferredcites →https://doi.org/10.1038/s41467-024-46150-wopenalex-snapshot
Data leakage inflates prediction performance in connectome-based machine learning models
inferredcites →https://doi.org/10.1071/wf21131openalex-snapshot
What determines variation in remotely sensed fire severity? Consideration of remote sensing limitations and confounding factors
inferredcites →https://doi.org/10.1111/brv.13041openalex-snapshot
Identifying and managing disturbance‐stimulated flammability in woody ecosystems
inferredcites →https://doi.org/10.32604/cmes.2025.073513openalex-snapshot
XGBoost-Based Active Learning for Wildfire Risk Prediction
inferredcites →https://doi.org/10.3390/computation14050115openalex-snapshot
Quantifying Domain-Specific Risk Signals in Lung Cancer Severity Prediction: A Multi-Domain Ablation Study Using XGBoost and SHAP
inferredcites →https://doi.org/10.3390/jcm15135141openalex-snapshot
Predicting the Risk of Cardiovascular Diseases in the Elderly Based on Clinical Data and Heart Rate Variability Using Machine Learning
inferredcites →https://doi.org/10.3390/s25195965openalex-snapshot
A Comprehensive Review of Remote Sensing and Artificial Intelligence Integration: Advances, Applications, and Challenges
inferredcites →https://doi.org/10.5532/kjafm.2012.14.1.001openalex-snapshot
Prediction of Forest Fire Danger Rating over the Korean Peninsula with the Digital Forecast Data and Daily Weather Index (DWI) Model
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.