Zobrazeno 1 - 10
of 238
pro vyhledávání: '"Chaopeng Shen"'
Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors
Autor:
Md Abdullah Al Mehedi, Shah Saki, Krutikkumar Patel, Chaopeng Shen, Sagy Cohen, Virginia Smith, Adnan Rajib, Emmanouil Anagnostou, Tadd Bindas, Kathryn Lawson
Publikováno v:
Earth's Future, Vol 12, Iss 7, Pp n/a-n/a (2024)
Abstract Manning's roughness coefficient, n, is used to describe channel roughness, and is a widely sought‐after key parameter for estimating and predicting flood propagation. Due to its control of flow velocity and shear stress, n is critical for
Externí odkaz:
https://doaj.org/article/3ff04120942443dcb07a036b61646fa1
Autor:
Savinay Nagendra, Daniel Kifer, Benjamin Mirus, Te Pei, Kathryn Lawson, Srikanth Banagere Manjunatha, Weixin Li, Hien Nguyen, Tong Qiu, Sarah Tran, Chaopeng Shen
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 4349-4370 (2022)
Recent small-scale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In th
Externí odkaz:
https://doaj.org/article/2d90ab8149a54d9eb8bdeed86d342002
Autor:
Wen-Ping Tsai, Dapeng Feng, Ming Pan, Hylke Beck, Kathryn Lawson, Yuan Yang, Jiangtao Liu, Chaopeng Shen
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
Much effort is invested in calibrating model parameters for accurate outputs, but established methods can be inefficient and generic. By learning from big dataset, a new differentiable framework for model parameterization outperforms state-of-the-art
Externí odkaz:
https://doaj.org/article/79ba5184e4a24bf6ac1ca15f35085535
Autor:
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, Massimiliano Zappa
Publikováno v:
Hydrology and Earth System Sciences, 27 (9)
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety ofpredictions from dynamical, physics-based models - such as numerical weather prediction, climate, land, h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ccb2284832e8b329ecde25af75f308a0
https://doi.org/10.5194/hess-27-1865-2023
https://doi.org/10.5194/hess-27-1865-2023
Publikováno v:
Nature Water. 1:249-260
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::79f27fe6ec00d73e8eb73299bed41fa9
https://doi.org/10.1038/s44221-023-00038-z
https://doi.org/10.1038/s44221-023-00038-z
Publikováno v:
Frontiers in Water, Vol 3 (2021)
Externí odkaz:
https://doaj.org/article/75041d1b26c444bd8154147fbab87c03
Publikováno v:
Water, Vol 14, Iss 9, p 1429 (2022)
A warming climate will intensify the water cycle, resulting in an exacerbation of water resources crises and flooding risks in the Lancang–Mekong River Basin (LMRB). The mitigation of these risks requires accurate streamflow and flood simulations.
Externí odkaz:
https://doaj.org/article/263f32d09b6547ab9a7c0dd90725216d
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
Autor:
Tadd Bindas, Wen-Ping Tsai, Jiangtao Liu, Farshid Rahmani, Dapeng Feng, Yuchen Bian, Kathryn Lawson, Chaopeng Shen
Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models — particularly, a genre called differentiable modeling that intermingles N
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b8214d406d6c888226c869d63fc3791b
https://doi.org/10.22541/essoar.168500246.67971832/v1
https://doi.org/10.22541/essoar.168500246.67971832/v1
Autor:
Tadd Bindas, Wen-Ping Tsai, Jiangtao Liu, Farshid Rahmani, Dapeng Feng, Yuchen Bian, Kathryn Lawson, Chaopeng Shen
Differentiable modeling has been introduced recently as a method to learn relationships from a combination of data and structural priors. This method uses end-to-end gradient tracking inside a process-based model to tune internal states and parameter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d20ccf680f49ff58c3d8f8b311cb16c3
https://doi.org/10.5194/egusphere-egu23-16658
https://doi.org/10.5194/egusphere-egu23-16658