This paper presents a novel approach to enhancing the accuracy of precipitation estimation in Central Vietnam using the Extreme Gradient Boosting (XGBoost) machine learning model. The proposed method integrates multi-source data, combining satellite imagery from Himawari-8, atmospheric reanalysis from ERA-5, and digital elevation models from ASTER DEM to train the model.
Nội dung trích xuất từ tài liệu:
Improving precipitation estimation accuracy for the Central Vietnam region using the XGBoost model with multi-source data
Improving precipitation estimation accuracy for the Central Vietnam region using the XGBoost model with multi-source data
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Rainfall estimation Machine learning XGBoost model Himawari-8 satellite data Digital elevation modelTài liệu có liên quan:
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