Machine learning | Sukanta Basu

2026

S2G-DI: A Deep Learning Framework for Generating High-Resolution Wind Gust Fields from Sparse Mesonet Observations

H Baki, M Pierzyna, and S Basu

Artificial Intelligence for the Earth Systems, 2026

under revision

@article{baki2026AIES_ml,
  title = {{S2G-DI}: A Deep Learning Framework for Generating High-Resolution Wind Gust Fields from Sparse Mesonet Observations},
  author = {Baki, H and Pierzyna, M and Basu, S},
  journal = {Artificial Intelligence for the Earth Systems},
  year = {2026},
  note = {under revision}
}

2024

Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using TabNet

H Baki and S Basu

Environmental Data Science, 2024

Bib

@article{baki2024EDS_ml,
  title = {Estimating high-resolution profiles of wind speeds from a global reanalysis dataset using {TabNet}},
  author = {Baki, H and Basu, S},
  journal = {Environmental Data Science},
  year = {2024},
  volume = {3: e32},
  pages = {1--10},
}

2023

A decision tree-based measure-correlate-predict approach for peak wind gust estimation from a global reanalysis dataset

S Kartal, S Basu, and S J Watson

Wind Energy Science, 2023

Bib

@article{kartal2023WES_ml,
  title = {A decision tree-based measure-correlate-predict approach for peak wind gust estimation from a global reanalysis dataset},
  author = {Kartal, S and Basu, S and Watson, S J},
  journal = {Wind Energy Science},
  year = {2023},
  volume = {8},
  pages = {1533--1551},
}

2022

Automated identification of “Dunkelflaute" events: A convolutional neural network-based autoencoder approach

B Li, S Basu, and S J Watson

Artificial Intelligence for the Earth Systems, 2022

Bib

@article{li2022_ml,
  title = {Automated identification of {``Dunkelflaute"} events: A convolutional neural network-based autoencoder approach},
  author = {Li, B and Basu, S and Watson, S J},
  journal = {Artificial Intelligence for the Earth Systems},
  year = {2022},
  volume = {1: e220015},
  pages = {1--17},
}