Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Kangling Lin"'
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4601 (2023)
Satellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper propose
Externí odkaz:
https://doaj.org/article/5f94880cb2064aef90afe053bf431dae
Publikováno v:
Remote Sensing, Vol 15, Iss 12, p 3135 (2023)
To improve the accuracy and reliability of precipitation estimation, numerous models based on machine learning technology have been developed for integrating data from multiple sources. However, little attention has been paid to extracting the spatio
Externí odkaz:
https://doaj.org/article/a229cb4cbd2c4bc09bfa4ff45777ba2c
Autor:
Kangling Lin, Sheng Sheng, Yanlai Zhou, Feng Liu, Zhiyu Li, Hua Chen, Chong-Yu Xu, Jie Chen, Shenglian Guo
Publikováno v:
Hydrology Research, Vol 51, Iss 5, Pp 1136-1149 (2020)
The Temporal Convolutional Network (TCN) and TCN combined with the Encoder-Decoder architecture (TCN-ED) are proposed to forecast runoff in this study. Both models are trained and tested using the hourly data in the Jianxi basin, China. The results i
Externí odkaz:
https://doaj.org/article/19885750ccf9450c99d95b3d4eacaf6f
Publikováno v:
Remote Sensing; Volume 15; Issue 12; Pages: 3135
To improve the accuracy and reliability of precipitation estimation, numerous models based on machine learning technology have been developed for integrating data from multiple sources. However, little attention has been paid to extracting the spatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::12d7452a77b2de7483da2c3c1b4dbb74
Publikováno v:
Science of The Total Environment. 891:164494
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03f2cceec27c8698b8f4f96f5ae95995
https://doi.org/10.1016/j.scitotenv.2023.164494
https://doi.org/10.1016/j.scitotenv.2023.164494
Autor:
Jie Chen, Yanlai Zhou, Chong-Yu Xu, Sheng Sheng, Zhiyu Li, Feng Liu, Kangling Lin, Shenglian Guo, Hua Chen
Publikováno v:
Hydrology Research, Vol 51, Iss 5, Pp 1136-1149 (2020)
The Temporal Convolutional Network (TCN) and TCN combined with the Encoder-Decoder architecture (TCN-ED) are proposed to forecast runoff in this study. Both models are trained and tested using the hourly data in the Jianxi basin, China. The results i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c940ca1eb877010fc335078466269ee
http://hr.iwaponline.com/content/51/5/1136
http://hr.iwaponline.com/content/51/5/1136
Making accurate and reliable probability density forecasts of flood processes is fundamentally challenging for machine learning techniques, especially when prediction targets are outside the range of training data. Conceptual hydrological models can
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb1c8bba5c7e48e727018fee8b169d88
http://hdl.handle.net/10852/100066
http://hdl.handle.net/10852/100066
With the rapid growth of deep learning recently, artificial neural networks have been propelled to the forefront in flood forecasting via their end-to-end learning ability. Encoder-decoder architecture, as a novel deep feature extraction, which captu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a84fc016a2bb7d211257283ec7b4d33
https://doi.org/10.5194/egusphere-egu2020-6277
https://doi.org/10.5194/egusphere-egu2020-6277