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High-Precision Spatial Interpolation of Meteorological Variables in Complex Terrain Using Machine Learning Methods
Shuangping Li1, Bin Zhang1, Bo Shi1
1Changjiang Spatial Information Technology Engineering Co., Ltd., Wuhan 430010, China.
Sensors (Basel, Switzerland)
|April 14, 2026
Summary
Machine learning, particularly the enhanced XGB-C model, significantly improves meteorological interpolation accuracy in complex terrain for deformation monitoring. This method outperforms traditional techniques, offering precise atmospheric delay corrections.
Area of Science:
- Geosciences and Environmental Science
- Computer Science and Artificial Intelligence
- Geodesy and Surveying
Background:
- Traditional spatial interpolation methods like Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) struggle with complex topography.
- Accurate meteorological data is crucial for high-precision deformation monitoring in environments like hydropower stations.
- Existing methods lack the ability to capture fine-scale atmospheric variations in challenging terrains.
Purpose of the Study:
- To evaluate the effectiveness of machine learning algorithms for high-precision spatial interpolation of meteorological variables.
- To compare the performance of Random Forest (RF) and eXtreme Gradient Boosting (XGBoost, XGB) against traditional methods.
- To introduce and validate an enhanced XGBoost model (XGB-C) for improved atmospheric delay corrections.
Main Methods:
- Systematic comparison of RF, XGBoost, and traditional IDW/OK methods for interpolating temperature, humidity, and pressure.
- Development of an enhanced XGBoost model (XGB-C) treating spatial interpolation as a supervised learning problem.
- Performance evaluation using RMSE, MAE, and R² metrics with daily meteorological data from 47 stations (2023-2024).
Main Results:
- Machine learning methods significantly outperformed traditional interpolation approaches.
- The proposed XGB-C model achieved the highest accuracy: R² ≈ 1.00 for pressure, 0.97 for humidity, and 0.83 for temperature.
- Interpolation accuracy varied with seasons and land cover, showing greater challenges in summer and in "Urban and Built-Up" and "Croplands" areas.
Conclusions:
- Machine learning, especially the XGB-C model, offers substantial advantages for meteorological interpolation in complex mountainous environments.
- Accurate atmospheric corrections derived from ML interpolation are vital for enhancing deformation monitoring accuracy.
- The study provides a foundation for operational ML-based interpolation models using UAV remote sensing data.
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