Zobrazeno 1 - 10
of 209
pro vyhledávání: '"Zhaoli, Wang"'
Publikováno v:
Journal of Hydrology: Regional Studies, Vol 52, Iss , Pp 101739- (2024)
Study region: Yangtze River Delta core urban agglomeration, China Study focus: Traditional research on flood susceptibility assessment using machine learning often seeks to enhance model performance by increasing the number of input variables, which
Externí odkaz:
https://doaj.org/article/a8cb1a3806d14b0697b2142eb8da2f10
Autor:
Xiangdong Lei, Jie Jiang, Zifeng Deng, Di Wu, Fangyi Wang, Chengguang Lai, Zhaoli Wang, Xiaohong Chen
Publikováno v:
Remote Sensing, Vol 16, Iss 12, p 2246 (2024)
Urban reservoirs contribute significantly to human survival and ecological balance. Machine learning-based remote sensing techniques for monitoring water quality parameters (WQPs) have gained increasing prominence in recent years. However, these tech
Externí odkaz:
https://doaj.org/article/5a1353d62f8f43878b983606af525869
Autor:
YUNTAO LI1, ZHAOLI WANG1,2 liyuntao66552023@163.com, QI ZHANG1,2, LIN SHEN1,3, YANG ZHANG1,4, YUE GUO1,4
Publikováno v:
Indian Journal of Pharmaceutical Sciences. Mar/Apr2024, Vol. 86 Issue 2, p468-475. 8p.
Publikováno v:
Shipin gongye ke-ji, Vol 44, Iss 14, Pp 457-464 (2023)
The type and content of organic acids have important influence on the quality of fruit wine. Proper amount of organic acids can give fruit wine soft taste, and has certain antibacterial effect. However, higher organic acid content causes the unpleasa
Externí odkaz:
https://doaj.org/article/2426cd43b26a48a591f501395ae5e0d6
Publikováno v:
Archives of Civil Engineering, Vol vol. 69, Iss No 2, Pp 399-416 (2023)
The rheological property of asphalt is an important factor affecting the pavement performance of asphalt binder, and the fundamental reason for the change of asphalt rheological property is the strong evolution of asphalt material meso structure. How
Externí odkaz:
https://doaj.org/article/84f0d1159a7c466c86ccf23d5907d4b4
Autor:
Jun Li, Emanuele Bevacqua, Zhaoli Wang, Stephen Sitch, Vivek Arora, Almut Arneth, Atul K. Jain, Daniel Goll, Hanqin Tian, Jakob Zscheischler
Publikováno v:
Communications Earth & Environment, Vol 4, Iss 1, Pp 1-10 (2023)
Abstract Gross primary production is the basis of global carbon uptake. Gross primary production losses are often related to hydroclimatic extremes such as droughts and heatwaves, but the trend of such losses driven by hydroclimatic extremes remains
Externí odkaz:
https://doaj.org/article/47b4604309ce47c0bafc4ddeadf174af
Publikováno v:
Agricultural Water Management, Vol 291, Iss , Pp 108649- (2024)
With global climate warming, the variability of climate and weather tends to increase driving the water resources available for vegetation become more uncertain. Therefore, there is still a debate as to how vegetation response to, and to which extent
Externí odkaz:
https://doaj.org/article/78ae37ef1fcd468a9454a9d2555c6f2d
Dependence of daily precipitation and wind speed over coastal areas: evidence from China's coastline
Publikováno v:
Hydrology Research, Vol 54, Iss 4, Pp 491-507 (2023)
Rainfall and wind speed are two important meteorological variables that have a significant impact on agriculture, human health, and socio-economic development. While individual rainfall or wind events have been widely studied, little attention has be
Externí odkaz:
https://doaj.org/article/aaa35cf07a2544e7bd57ee4c89076e37
Publikováno v:
International Journal of Disaster Risk Science, Vol 14, Iss 2, Pp 253-268 (2023)
Abstract Fast and accurate prediction of urban flood is of considerable practical importance to mitigate the effects of frequent flood disasters in advance. To improve urban flood prediction efficiency and accuracy, we proposed a framework for fast m
Externí odkaz:
https://doaj.org/article/04b3ea11b6c24cf58a9e15d36a68f18f
Autor:
Xuezhi Tan, Qiying Mai, Guixing Chen, Bingjun Liu, Zhaoli Wang, Chengguang Lai, Xiaohong Chen
Publikováno v:
Journal of Hydrology: Regional Studies, Vol 46, Iss , Pp 101327- (2023)
Study region: The Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China. Study focus: Using hourly rain gauge data and CMORPH data, we use the duration-dependent generalized extreme value (d-GEV) model and the scaling invariant GEV model inferred b
Externí odkaz:
https://doaj.org/article/ea4c1f441b274285a1f51378458418ed