Analysis of Machine Learning Performance in Spatial Interpolation of Rainfall Data
The spatialization of precipitation data is crucial for studies on climatology, agriculture, and climate change, as well as for urban and environmental planning. Established spatial interpolation methods such as Inverse Distance Weighting are widely used for this purpose. With technological advancem...
Saved in:
| Main Authors: | Alexandre E. L. Nobrega, Itamir M. Barroca Filho |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11002472/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Spatial Interpolation Methods of Temperature Data Based on Geographic Information System—Taking Jiangxi Province as an Example
by: Zihao Feng, et al.
Published: (2024-12-01) -
Machine learning and topological kriging for river water quality data interpolation
by: Rokhana Dwi Bekti, et al.
Published: (2025-02-01) -
Comparative analysis of interpolation methods for rainfall mapping in the Faria catchment, Palestine
by: Sameer Shadeed, et al.
Published: (2021-12-01) -
Métodos de interpolação espacial para o mapeamento da precipitação pluvial Spatial interpolation methods for mapping of rainfall
by: Marcelo R. Viola, et al.
Published: (2010-09-01) -
Approach to adaptive spatial interpolation of geophysical information
by: A.V. Vorobev, et al.
Published: (2025-04-01)