Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain.

Zhang, Gangqiang; Zheng, Wei; Yin, Wenjie; Lei, Weiwei

Zhang, Gangqiang; Zheng, Wei; Yin, Wenjie; Lei, Weiwei.

Afiliação

Zhang G; School of Surveying and Landing Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China.
Zheng W; Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China.
Yin W; School of Surveying and Landing Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China.
Lei W; Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China.

Sensors (Basel) ; 21(1)2020 Dec 24.

Article em En | MEDLINE | ID: mdl-33374144

Palavras-chave

GRACE; North China Plain; gradient boosting decision tree; groundwater level anomalies; machine learning-based fusion model; statistical downscaling

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China

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