$ cat research/publications.bib

# research

Peer-reviewed work. One paper so far — more in the pipeline.
── publication · 01
“Using Random Forest for Future Sea Level Prediction”
authors
Haolun Ding
venue
SHS Web of Conferences · Vol. 174 · art. 03008
year
2023
status
peer-reviewed · published
doi
abstract
A machine-learning approach to forecasting global sea-level rise using Random Forest regression. Historical tide-gauge and satellite-altimetry data were used to train a predictive model; hyperparameter tuning was applied to improve accuracy. The results suggest Random Forest can serve as a viable, interpretable alternative to classical statistical sea-level models for medium-horizon forecasting.
methods
› Tide-gauge & altimetry preprocessing
› Random Forest regression (scikit-learn)
› Grid-search hyperparameter tuning
› Cross-validated accuracy evaluation
#random-forest#sea-level-rise#hyperparameter-tuning#climate-ml#regression
bibtex
@article{ding2023sealevel,
  title   = {Using Random Forest for Future Sea Level Prediction},
  author  = {Ding, Haolun},
  journal = {SHS Web of Conferences},
  volume  = {174},
  pages   = {03008},
  year    = {2023},
  doi     = {10.1051/shsconf/202317403008}
}
→ read full paper on doi.org
── in the pipeline
Ongoing HCI research and coursework will surface here once published. Watch this space.