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In this work, we present the Temporal Fusion Transformer-Gaussian Process (TFT-GP), a novel model for multi-horizon probabilistic river level prediction. We show how TFT-GP inherits the nice properties of the Gaussian process and deep neural networks, giving it excellent representative power and uncertainty quantification ability. The performance of TFT-GP is thoroughly compared with existing well-known deep learning models in three real-world hydrological datasets, and the results showed that TFT-GP is not only more accurate in point prediction but also more reasonable in uncertainty quantification. <\/jats:p>","DOI":"10.1142\/s0218126623503097","type":"journal-article","created":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T08:15:40Z","timestamp":1682842540000},"source":"Crossref","is-referenced-by-count":5,"title":["Temporal Fusion Transformer-Gaussian Process for Multi-Horizon River Level Prediction and Uncertainty Quantification"],"prefix":"10.1142","volume":"32","author":[{"given":"Cheng","family":"Wang","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310014, P. R. China"},{"name":"Taizhou Research Institute, Zhejiang University of Technology, Taizhou, 318001, P. R. 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