Zobrazeno 1 - 10
of 1 427
pro vyhledávání: '"A Chehbouni"'
In an effort to mitigate the harms of large language models (LLMs), learning from human feedback (LHF) has been used to steer LLMs towards outputs that are intended to be both less harmful and more helpful. Despite the widespread adoption of LHF in p
Externí odkaz:
http://arxiv.org/abs/2411.08243
Autor:
Chehbouni, Khaoula, Roshan, Megha, Ma, Emmanuel, Wei, Futian Andrew, Taik, Afaf, Cheung, Jackie CK, Farnadi, Golnoosh
Recent progress in large language models (LLMs) has led to their widespread adoption in various domains. However, these advancements have also introduced additional safety risks and raised concerns regarding their detrimental impact on already margin
Externí odkaz:
http://arxiv.org/abs/2403.13213
The integration of remote sensing and machine learning in agriculture is transforming the industry by providing insights and predictions through data analysis. This combination leads to improved yield prediction and water management, resulting in inc
Externí odkaz:
http://arxiv.org/abs/2306.04566
Publikováno v:
Journal of Water and Climate Change, Vol 15, Iss 4, Pp 1588-1611 (2024)
In Morocco, the historical record depicts a situation characterized by increasing temperatures and diminishing precipitation, which often ends up in severe drought episodes. This research examines the vulnerability of wheat, barley, and maize to grow
Externí odkaz:
https://doaj.org/article/2d3b8f8f84c4415ab25fbb03f65b2939
Autor:
Lhoussaine El Mezouary, Abdessamad Hadri, Mohamed Hakim Kharrou, Younes Fakır, Abderrahman Elfarchouni, Lhoussaine Bouchaou, Abdelghani Chehbouni
Publikováno v:
Applied Water Science, Vol 14, Iss 5, Pp 1-26 (2024)
Abstract Groundwater resources in Morocco often face sustainability challenges due to increased exploitation and climate change. Specifically, the Al-Haouz-Mejjate groundwater in the Marrakesh region is faced with overexploitation and insufficient re
Externí odkaz:
https://doaj.org/article/725236e2be054267b4b42740e45d2101
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 16, Pp n/a-n/a (2024)
Abstract In this study, prominent dust source areas are identified along with their plume extent using high temporal frequency satellite observations. Hourly dust plume observations of the Dust Belt from geostationary‐orbit satellites are analyzed
Externí odkaz:
https://doaj.org/article/73117defa7e244309ee6cd69ddceb35c
Autor:
Oumar Jaffar, Abdessamad Hadri, El Mahdi El Khalki, Khaoula Ait Naceur, Mohamed Elmehdi Saidi, Yves Tramblay, Abdelghani Chehbouni
Publikováno v:
Journal of Hydrology: Regional Studies, Vol 54, Iss , Pp 101899- (2024)
Study region: 30 catchments in Morocco. Study focus: We assessed the KGE performance of eight monthly lumped hydrological models forced by ground-based rainfall observations. We then examined how the performance relates to model complexity and struct
Externí odkaz:
https://doaj.org/article/2e9211e61a914782bdaf9e94a2e9ddd9
Autor:
Ourrai Sara, Aithssaine Bouchra, Amazirh Abdelhakim, Er-RAKI Salah, Bouchaou Lhoussaine, Jacob Frederic, Chehbouni Abdelghani
Publikováno v:
Agricultural Water Management, Vol 298, Iss , Pp 108861- (2024)
Olives constitute a frequently grown crop in semi-arid areas. Therefore, accurate quantification of evapotranspiration (ET) within olive groves is crucial to enhance agricultural water productivity and promote their resilience to water scarcity and f
Externí odkaz:
https://doaj.org/article/b53a573fef2440f0ae28957b21b5b608
Autor:
Rafi, Zoubair, Dantec, Valérie Le, Khabba, Saïd, Amazirh, Abdelhakim, Mordelet, Patrick, Bouras, El Houssaine, Er-Raki, Salah, Chehbouni, Abdelghani, Merlin, Olivier
Publikováno v:
In Agricultural and Forest Meteorology 15 November 2024 358
Autor:
Jamal Ezzahar, Abdelghani Chehbouni, Nadia Ouaadi, Mohammed Madiafi, Khabba Said, Salah Er-Raki, Ahmed Laamrani, Adnane Chakir, Zohra Lili Chabaane, Mehrez Zribi
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2256-2271 (2024)
This work aims to assess the effectiveness of machine learning (ML) algorithms and semiempirical models for surface soil moisture (SSM) retrieval by exploring the Sentinel-1 backscatter and interferometric coherence data. First, three commonly used c
Externí odkaz:
https://doaj.org/article/d4b8fc80765e4678a4caa402ecb76d75