Spatio-temporal DeepKriging in PyTorch: A Supplementary Application to Precipitation Data for Interpolation and Probabilistic Forecasting

Code

• github.com/pratiknag/spatio-temporaldeepkriging-pytorch
  OfficialIn paper★ 5
• github.com/pratiknag/pytorch-convlstm
  OfficialIn paper★ 4

Abstract

A detailed analysis of precipitation data over Europe is presented, with a focus on interpolation and forecasting applications. A Spatio-temporal DeepKriging (STDK) framework has been implemented using the PyTorch platform to achieve these objectives. The proposed model is capable of handling spatio-temporal irregularities while generating high-resolution interpolations and multi-step forecasts. Reproducible code modules have been developed as standalone PyTorch implementations for the interpolation [2]Interpolation - https://github.com/pratiknag/Spatio-temporalDeepKriging-Pytorch.git and forecasting [3]Forecasting - https://github.com/pratiknag/pytorch-convlstm.git, facilitating broader application to similar climate datasets. The effectiveness of this approach is demonstrated through extensive evaluation on daily precipitation measurements, highlighting predictive performance and robustness.

Reproductions