High-Precision Spatial Interpolation of Meteorological Variables in Complex Terrain Using Machine Learning Methods

作者：Shuo Li, Bin Zhang, Bo Shi, Qingsong Ai, Yuxi Zeng, Xuanyao Yan, Hao Chen, Huawei Wang · 发表于：Sensors · 年份：2026 · DOI：10.3390/s26072167 · 被引用次数：1 · 研究领域：Flood Risk Assessment and Management、Remote Sensing in Agriculture、Meteorological Phenomena and Simulations

This study has explored the effectiveness of machine learning methods for high-precision spatial interpolation of meteorological variables, aiming to provide accurate atmospheric delay corrections for high-precision edge and corner nets observation in complex-terrain environments such as the Xiluodu Hydropower Station, thereby enhancing the accuracy of deformation monitoring. Considering the significant limitations of traditional interpolation methods such as Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) in capturing spatial variability under complex topographic conditions, we systematically introduced machine learning algorithms including Random Forest (RF)and eXtreme Gradient Boosting (XGBoost, XGB) to compare their performance with traditional methods for high-density interpolation of sparsely distributed temperature, relative humidity, and surface pressure, respectively. Concurrently, we proposed an enhanced XGB model incorporating center-point features (XGB-C) which frames spatial interpolation as a supervised learning problem that learns physical mapping from synoptic backgrounds to local microclimates instead of relying on geometric distances alone. The interpolation performance indices (RMSE, MAE, and R2) were evaluated with daily meteorological observations from 47 stations (38 for training, 9 for testing) during 2023–2024. Results demonstrate that machine learning methods significantly outperform traditional approaches, with XGB-C achieving the highest ...