Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Kuai Fang"'
Publikováno v:
Frontiers in Water, Vol 2 (2020)
Some machine learning (ML) methods such as classification trees are useful tools to generate hypotheses about how hydrologic systems function. However, data limitations dictate that ML alone often cannot differentiate between causal and associative r
Externí odkaz:
https://doaj.org/article/0363a0c2a114445db237fb4fbe4caef8
Publikováno v:
Water, Vol 11, Iss 6, p 1202 (2019)
Groundwater hydraulic head (H) measurements and point-estimates of hydraulic conductivity (K) both contain information about the K field. There is no simple, a priori estimate of the relative worth of H and K data. Thus, there is a gap in our concept
Externí odkaz:
https://doaj.org/article/3629a18ffb08446e8be627ba32a928da
Publikováno v:
Water Resources Research. 58
When fitting statistical models to variables in geoscientific disciplines such as hydrology, it is a customary practice to regionalize - to divide a large spatial domain into multiple regions and study each region separately - instead of fitting a si
Autor:
Chaopeng Shen, Kuai Fang
Publikováno v:
Journal of Hydrometeorology. 21:399-413
Nowcasts, or near-real-time (NRT) forecasts, of soil moisture based on the Soil Moisture Active and Passive (SMAP) mission could provide substantial value for a range of applications including hazards monitoring and agricultural planning. To provide
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 57:2221-2233
The Soil Moisture Active Passive (SMAP) mission measures important soil moisture data globally. SMAP’s products might not always perform better than land surface models (LSM) when evaluated against in situ measurements. However, we hypothesize that
Autor:
Jerad D. Bales, Xiaodong Li, Fi-John Chang, Dongfeng Li, Wen-Ping Tsai, Kuai Fang, Chaopeng Shen, Daniel Kifer, Zheng Fang, Eric Laloy, Kuolin Hsu, Sanmay Ganguly, Amin Elshorbagy, Adrian Albert
Publikováno v:
Hydrology and Earth System Sciences, Vol 22, Pp 5639-5656 (2018)
Hydrology and Earth System Sciences, vol 22, iss 11
Shen, C; Laloy, E; Elshorbagy, A; Albert, A; Bales, J; Chang, F-J; et al.(2018). HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community. HYDROLOGY AND EARTH SYSTEM SCIENCES, 22(11), 5639-5656. doi: 10.5194/hess-22-5639-2018. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/7wt7z02g
Hydrology and Earth System Sciences, vol 22, iss 11
Shen, C; Laloy, E; Elshorbagy, A; Albert, A; Bales, J; Chang, F-J; et al.(2018). HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community. HYDROLOGY AND EARTH SYSTEM SCIENCES, 22(11), 5639-5656. doi: 10.5194/hess-22-5639-2018. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/7wt7z02g
Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far bee
Watersheds in the world are often perceived as being unique from each other, requiring customized study for each basin. Models uniquely built for each watershed, in general, cannot be leveraged for other watersheds. It is also a customary practice in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a939458d3f9f2b915e3d305536c28dd2
https://doi.org/10.5194/egusphere-egu21-16108
https://doi.org/10.5194/egusphere-egu21-16108
Publikováno v:
Water Resources Research. 56
Recently, recurrent deep networks have shown promise to harness newly available satellite-sensed data for long-term soil moisture projections. However, to be useful in forecasting, deep networks mu...
Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow mod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c90c95f6325661b7dbd0a2a9a0ab394
http://arxiv.org/abs/1912.08949
http://arxiv.org/abs/1912.08949