Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Yanlai Zhou"'
Publikováno v:
Hydrology Research, Vol 55, Iss 2, Pp 144-160 (2024)
The optimization operation of reservoir seasonal Flood-Limited Water Levels (FLWLs) can counterbalance the hydropower generation and flood prevention in the flood season. This study proposes a multi-objective optimization operation model to optimize
Externí odkaz:
https://doaj.org/article/d6833c2a22cc4abf95b42366f2ef3fbf
Publikováno v:
Energies, Vol 17, Iss 21, p 5485 (2024)
Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativenes
Externí odkaz:
https://doaj.org/article/0eacf0751a3f4d11b2c1f42132451286
Publikováno v:
Hydrology Research, Vol 54, Iss 4, Pp 606-632 (2023)
Rainfall characteristics are changing due to several reasons and change/trend detection is required. Literature survey reveals many relevant studies whose outcomes are divergent, possibly because different data series and different methodologies have
Externí odkaz:
https://doaj.org/article/50af86c8efb24f9990f4afe970a27249
Publikováno v:
Hydrology Research, Vol 53, Iss 2, Pp 259-278 (2022)
Quantifying the uncertainty of non-stationary flood frequency analysis is very crucial and beneficial for planning and design of water engineering projects, which is fundamentally challenging especially in the presence of high climate variability and
Externí odkaz:
https://doaj.org/article/78aab28ed4cf4ebeabeeb3ac6bdc634e
Publikováno v:
Hydrology Research, Vol 52, Iss 6, Pp 1436-1454 (2021)
The conceptual hydrologic model has been widely used for flood forecasting, while long short-term memory (LSTM) neural network has been demonstrated a powerful ability to tackle time-series predictions. This study proposed a novel hybrid model by com
Externí odkaz:
https://doaj.org/article/14b194efae674491a95204e87b5a1154
Publikováno v:
Hydrology Research, Vol 53, Iss 7, Pp iii-v (2022)
Externí odkaz:
https://doaj.org/article/c6d75538a1064792a4af25b31004b9c0
Publikováno v:
Earth and Space Science, Vol 9, Iss 3, Pp n/a-n/a (2022)
Abstract Detection and attribution of precipitation variability are fundamentally challenging, especially in the presence of complex nonlinear relationships between precipitation variability and large‐scale teleconnections. The aim of this study is
Externí odkaz:
https://doaj.org/article/32d20566640d40febe2034f6c6988993
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:
Hydrology Research, Vol 51, Iss 2, Pp 366-380 (2020)
There has been a surge of interest in the field of urban flooding in recent years. However, current stormwater management models are often too complex to apply on a large scale. To fill this gap, we use a physically based and spatially distributed ov
Externí odkaz:
https://doaj.org/article/59d65ba34ca6431d8e16ab6d56431bcb
Publikováno v:
Hydrology and Earth System Sciences. 26:4853-4874
Studies on the hydrological response to continuous extreme and asymptotic climate change can improve our ability to cope with intensified water-related problems. Most of the literature focused on the runoff response to climate change, while neglectin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9f0de546eeb6a4b426ea6b5c400dad5
https://doi.org/10.5194/hess-26-4853-2022
https://doi.org/10.5194/hess-26-4853-2022