Spatial Interpolation
15 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Spatial Interpolation
Latest papers with no code
Thermal Earth Model for the Conterminous United States Using an Interpolative Physics-Informed Graph Neural Network (InterPIGNN)
This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop national temperature-at-depth maps for the conterminous United States.
Uncertainty estimation in spatial interpolation of satellite precipitation with ensemble learning
This demonstrates the potential of stacking to improve probabilistic predictions in spatial interpolation and beyond.
Experimental Study of Spatial Statistics for Ultra-Reliable Communications
Using experimental channel measurements from 127 locations, we demonstrate the use case of providing statistical guarantees for rate selection in ultra-reliable low-latency communication (URLLC) using CDI maps.
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation
We show that the proposed data augmentation strategy helps enhance the performance of the state-of-the-art convolutional neural network-random forest (CNN-RF) model by a reasonable amount, resulting in a noteworthy improvement in spatial correlation and a reduction in prediction error.
ESTformer: Transformer Utilizing Spatiotemporal Dependencies for EEG Super-resolution
The ESTformer, with the fixed masking strategy, adopts a mask token to up-sample the low-resolution (LR) EEG data in case of disturbance from mathematical interpolation methods.
Improving Real Estate Appraisal with POI Integration and Areal Embedding
Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges.
Uncertainty estimation in satellite precipitation interpolation with machine learning
Compared to QR, LightGBM showed improved performance with respect to the quantile scoring rule by 11. 10%, followed by QRF (7. 96%), GRF (7. 44%), GBM (4. 64%) and QRNN (1. 73%).
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields
Large-scale spatial interpolation or downscaling of bivariate wind fields having velocity in two dimensions is a challenging task because wind data tend to be non-Gaussian with high spatial variability and heterogeneity.
Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles
To improve precipitation estimates, machine learning is applied to merge rain gauge-based measurements and gridded satellite precipitation products.
Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network: An Indian Case Study
A thorough analysis of data collected for seven months has been presented to establish the need for dense deployment of PM monitoring devices.
