Zobrazeno 1 - 10
of 3 254
pro vyhledávání: '"regional scale"'
Autor:
Meiling Sheng, Chunlin Li, Weixing Zhang, Jing Nie, Hao Hu, Weidong Lou, Xunfei Deng, Shengzhi Shao, Xiaonan Lyu, Zhouqiao Ren, Karyne M. Rogers, Syed Abdul Wadood, Yongzhi Zhang, Yuwei Yuan
Publikováno v:
Food Frontiers, Vol 5, Iss 5, Pp 2188-2198 (2024)
Abstract Effective geographical origin discrimination of Chinese rice requires a large database of samples to ensure sufficient data for origin verification at a regional scale. In this study, environmental similarity was used to establish a spatial
Externí odkaz:
https://doaj.org/article/cf78e49eb6ae47f797371113c9da8b17
Publikováno v:
Journal of Maps, Vol 20, Iss 1, Pp 1-14 (2024)
ABSTRACTIn mountain regions, the impact of areas on the sediment conveyance can not only be described by their susceptibility to debris flow release, but also by their structural connectivity to the rivers. This generates the need to combine suscepti
Externí odkaz:
https://doaj.org/article/264c0d43fcd948269e5dd8bb21be01c7
Publikováno v:
Shuitu Baochi Xuebao, Vol 38, Iss 4, Pp 72-82 (2024)
[Objective] Soil and water conservation is important in protecting and improving the ecological environment, which plays an important role for the sustainable development of ecosystems. [Methods] While the study of the soil and water conservation eff
Externí odkaz:
https://doaj.org/article/71b3537f4352459f8a6a6738088890e0
Autor:
Brian Nathan, Irène Xueref-Remy, Thomas Lauvaux, Christophe Yohia, Damien Piga, Jacques Piazzola, Tomohiro Oda, Mélissa Milne, Maria Herrmann, Cathy Wimart-Rousseau, Alexandre Armengaud
Publikováno v:
Atmosphere, Vol 15, Iss 10, p 1193 (2024)
As atmospheric CO2 emissions and the trend of urbanization both increase, the ability to accurately assess the CO2 budget from urban environments becomes more important for effective CO2 mitigation efforts. This task can be difficult for complex area
Externí odkaz:
https://doaj.org/article/9ad8f12174864a27aa9b63d53a3c3bb6
Autor:
Marina Sanz-Martín, Manuel Hidalgo, Patricia Puerta, Jorge García Molinos, Marina Zamanillo, Isaac Brito-Morales, José Manuel González-Irusta, Antonio Esteban, Antonio Punzón, Encarnación García-Rodríguez, Miguel Vivas, Lucía López-López
Publikováno v:
Ecological Indicators, Vol 160, Iss , Pp 111741- (2024)
The Mediterranean Sea is one of the most vulnerable ecosystems in the world due to the variety and severity of cumulative impacts faced, including high climate risk. Species distributions are expected to track climate niches in response to warming, w
Externí odkaz:
https://doaj.org/article/43fe5c72c2c444599566567ebc7b2793
Publikováno v:
Climate, Vol 12, Iss 8, p 127 (2024)
Climate change and rising sea levels pose significant threats to coastal regions, necessitating accurate and timely forecasts. Current methods face limitations due to their inability to fully capture nonlinear complexities, high computational costs,
Externí odkaz:
https://doaj.org/article/95d817a0970c454b8c1e0b6b981bf455
Autor:
Nima Mirhadi, Renato Macciotta
Publikováno v:
Geosciences, Vol 14, Iss 7, p 194 (2024)
This work illustrates a semi-quantitative approach to evaluate changes in regional landslide distribution as a consequence of forecasted climate change, which can be adopted at other regions. We evaluated the relationship between climate conditions a
Externí odkaz:
https://doaj.org/article/711cffb92367407287571241131e9931
Autor:
Francisco Leitão
Publikováno v:
Oceans, Vol 4, Iss 3, Pp 220-235 (2023)
The influence of environmental variables (oceanographic and climatic) on the catch rates of striped red mullet (Mullus surmuletus) by artisanal fishery was investigated using different time series models (Dynamic Factorial Analyses; Min-Max Factorial
Externí odkaz:
https://doaj.org/article/675281aa71e74591a10f5e0faf915290
Publikováno v:
Geocarto International, Vol 38, Iss 1 (2023)
Landslide susceptibility prediction (LSP) is an important step for landslide hazard and risk assessment. Automated machine learning (AutoML) has the advantages of automatically features, models, and parameters selection. In this study, we proposed an
Externí odkaz:
https://doaj.org/article/1a416076098d4e12874dc3ca255d89b8
Autor:
Federico Mori, Daniele Spina, Flavio Bocchi, Amerigo Mendicelli, Giuseppe Naso, Massimiliano Moscatelli
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 14, Iss 1 (2023)
AbstractThe peak roof drift ratio is one of the most important engineering parameters to describe the expected seismic damage in a building. A predictive model of the drift ratio was developed using a machine learning approach (Gaussian process regre
Externí odkaz:
https://doaj.org/article/2ff6758a7df140298af2b91d955ab34b