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Performance and Efficiency of Climate In-Situ Data Reconstruction: Why Optimized IDW Outperforms kriging and Implicit Neural Representation

arXiv 2512.11832 ↗First posted 3 Dec 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

This study evaluates three reconstruction methods for sparse climate data: the simple inverse distance weighting (IDW), the statistically grounded ordinary kriging (OK), and the advanced implicit neural representation model (MMGN architecture). All methods were optimized through hyper-parameter tuning using validation splits. An extensive set of experiments was conducted, followed by a comprehensive statistical analysis. The results demonstrate the superiority of the simple IDW method over the other reference methods in terms of both reconstruction accuracy and computational efficiency. IDW achieved the lowest RMSE (3.00 ± 1.93), MAE (1.32 ± 0.77), and DeltaMAX (24.06 ± 17.15), as well as the highest R2 (0.68 ± 0.16), across 100 randomly sampled sparse datasets from the ECA&D database. Differences in RMSE, MAE, and R2 were statistically significant and exhibited moderate to large effect sizes. The Dunn post-hoc test further confirmed the consistent superiority of IDW across all evaluated quality measures [...]

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References · 9

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,472
  2. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,225
  3. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,492
  4. 2017Generative Adversarial Networks: An Overviewcited by 589
  5. 2017Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulationscited by 51
  6. 2022GIFS: Neural Implicit Function for General Shape Representationcited by 45
  7. 2022Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysiscited by 44
  8. 2024Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networkscited by 19
  9. 2024The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systemscited by 3

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