Zobrazeno 1 - 10
of 349
pro vyhledávání: '"hossein bonakdari"'
Publikováno v:
Applied Sciences, Vol 14, Iss 20, p 9209 (2024)
The accelerating impact of climate change on golf course conditions has led to a significant increase in pesticide dependency, underscoring the importance of innovative management strategies. The shift from Coupled Model Intercomparison Project Phase
Externí odkaz:
https://doaj.org/article/a2dafd125cfa414b84eafcd91753dd05
Autor:
Isa Ebtehaj, Hossein Bonakdari
Publikováno v:
Atmosphere, Vol 15, Iss 9, p 1082 (2024)
Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourl
Externí odkaz:
https://doaj.org/article/cb165d41004341469776379c0a228037
Publikováno v:
Energy and AI, Vol 16, Iss , Pp 100343- (2024)
Accurate short-term forecasting of heating energy demand is needed for achieving optimal building energy management, cost savings, environmental sustainability, and responsible energy consumption. Furthermore, short-term heating energy prediction con
Externí odkaz:
https://doaj.org/article/413325131ed04f038390118b01b90cb4
Publikováno v:
Journal of Applied Research in Water and Wastewater, Vol 10, Iss 2, Pp 119-132 (2023)
This research devised a cutting-edge artificial intelligence methodology to enhance flood forecasting in Quebec, Canada, an area frequently affected by floods. The core of this project was creating a novel artificial intelligence (AI) model (i.e., Ge
Externí odkaz:
https://doaj.org/article/67728676047348a8b1882ed63ef47b0e
Autor:
Hassan Alfaifi, Hossein Bonakdari
Publikováno v:
Fluids, Vol 9, Iss 9, p 198 (2024)
A new approach to predicting the geometrical characteristics of the mixing behavior of an inclined dense jet for angles ranging from 15° to 85° is proposed in this study. This approach is called the group method of data handling (GMDH) and is based
Externí odkaz:
https://doaj.org/article/09073bcc80304cbab921e2cb2d484d8b
Autor:
Keyvan Soltani, Afshin Amiri, Isa Ebtehaj, Hanieh Cheshmehghasabani, Sina Fazeli, Silvio José Gumiere, Hossein Bonakdari
Publikováno v:
Climate, Vol 12, Iss 8, p 119 (2024)
This study addresses the critical issue of drought zoning in Canada using advanced deep learning techniques. Drought, exacerbated by climate change, significantly affects ecosystems, agriculture, and water resources. Canadian Drought Monitor (CDM) da
Externí odkaz:
https://doaj.org/article/02eb131068964c53b9dfb87bd348f70c
Publikováno v:
Water, Vol 16, Iss 6, p 851 (2024)
Predicting morphological adjustments in alluvial meandering streams remains a challenging task due to the complex nature of the governing inter-related dynamic flow and sediment transport processes. This difficulty is increased in streams with irregu
Externí odkaz:
https://doaj.org/article/61320e3606044019a16a4f63d04fbba0
Autor:
Hossein Bonakdari, Jean-Pierre Pelletier, Francisco J. Blanco, Ignacio Rego-Pérez, Alejandro Durán-Sotuela, Dawn Aitken, Graeme Jones, Flavia Cicuttini, Afshin Jamshidi, François Abram, Johanne Martel-Pelletier
Publikováno v:
BMC Medicine, Vol 20, Iss 1, Pp 1-16 (2022)
Abstract Background Knee osteoarthritis is the most prevalent chronic musculoskeletal debilitating disease. Current treatments are only symptomatic, and to improve this, we need a robust prediction model to stratify patients at an early stage accordi
Externí odkaz:
https://doaj.org/article/c7c9c44f29fc4081a8ba1bf061d04e74
Publikováno v:
Frontiers in Water, Vol 5 (2023)
Real-time soil matric potential measurements for determining potato production's water availability are currently used in precision irrigation. It is well known that managing irrigation based on soil matric potential (SMP) helps increase water use ef
Externí odkaz:
https://doaj.org/article/24b1732ac61549c8b1bae82e2dac5080
Publikováno v:
Water Supply, Vol 22, Iss 5, Pp 5355-5375 (2022)
The Extreme Learning Machine (ELM) approach was used to predict stream health with a Qualitative Habitat Evaluation Index (QHEI), and watershed metrics. A dataset of 112 sites in Ontario, Canada with their Hilsenhoff Biotic Index (HBI) and richness v
Externí odkaz:
https://doaj.org/article/e47fd00272f747b7ac7ec38b3eaad094