Zobrazeno 1 - 10
of 263
pro vyhledávání: '"Shen Chaopeng"'
Few-shot segmentation is the problem of learning to identify specific types of objects (e.g., airplanes) in images from a small set of labeled reference images. The current state of the art is driven by resource-intensive construction of models for e
Externí odkaz:
http://arxiv.org/abs/2412.11998
Autor:
Ou, Zhigang, Nai, Congyi, Pan, Baoxiang, Pan, Ming, Shen, Chaopeng, Jiang, Peishi, Liu, Xingcai, Tang, Qiuhong, Li, Wenqing, Zheng, Yi
Reliable flood forecasting remains a critical challenge due to persistent underestimation of peak flows and inadequate uncertainty quantification in current approaches. We present DRUM (Diffusion-based Runoff Model), a generative AI solution for prob
Externí odkaz:
http://arxiv.org/abs/2412.11942
Autor:
Chang, Shuyu Y, Ghahremani, Zahra, Manuel, Laura, Erfani, Mohammad, Shen, Chaopeng, Cohen, Sagy, Van Meter, Kimberly, Pierce, Jennifer L, Meselhe, Ehab A, Goharian, Erfan
Hydraulic geometry parameters describing river hydrogeomorphic is important for flood forecasting. Although well-established, power-law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation wor
Externí odkaz:
http://arxiv.org/abs/2312.11476
Landslides are a recurring, widespread hazard. Preparation and mitigation efforts can be aided by a high-quality, large-scale dataset that covers global at-risk areas. Such a dataset currently does not exist and is impossible to construct manually. R
Externí odkaz:
http://arxiv.org/abs/2311.11138
For a number of years since its introduction to hydrology, recurrent neural networks like long short-term memory (LSTM) have proven remarkably difficult to surpass in terms of daily hydrograph metrics on known, comparable benchmarks. Outside of hydro
Externí odkaz:
http://arxiv.org/abs/2306.12384
Autor:
Shen, Chaopeng, Appling, Alison P., Gentine, Pierre, Bandai, Toshiyuki, Gupta, Hoshin, Tartakovsky, Alexandre, Baity-Jesi, Marco, Fenicia, Fabrizio, Kifer, Daniel, Li, Li, Liu, Xiaofeng, Ren, Wei, Zheng, Yi, Harman, Ciaran J., Clark, Martyn, Farthing, Matthew, Feng, Dapeng, Kumar, Praveen, Aboelyazeed, Doaa, Rahmani, Farshid, Beck, Hylke E., Bindas, Tadd, Dwivedi, Dipankar, Fang, Kuai, Höge, Marvin, Rackauckas, Chris, Roy, Tirthankar, Xu, Chonggang, Mohanty, Binayak, Lawson, Kathryn
Publikováno v:
Nat Rev Earth Environ 4, 552-567 (2023)
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and
Externí odkaz:
http://arxiv.org/abs/2301.04027
The purpose of binary segmentation models is to determine which pixels belong to an object of interest (e.g., which pixels in an image are part of roads). The models assign a logit score (i.e., probability) to each pixel and these are converted into
Externí odkaz:
http://arxiv.org/abs/2211.06560
Autor:
Khoshkalam, Yegane a, ⁎, Rousseau, Alain N. a, Rahmani, Farshid b, Shen, Chaopeng b, Abbasnezhadi, Kian c
Publikováno v:
In Journal of Hydrology April 2025 650
Publikováno v:
Water Resources Research, 58 (2022)
Predictions of hydrologic variables across the entire water cycle have significant value for water resource management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data-driven deep learning models
Externí odkaz:
http://arxiv.org/abs/2203.14827
River bathymetry is critical for many aspects of water resources management. We propose and demonstrate a bathymetry inversion method using a deep-learning-based surrogate for shallow water equations solvers. The surrogate uses the convolutional auto
Externí odkaz:
http://arxiv.org/abs/2203.02821