Zobrazeno 1 - 10
of 148
pro vyhledávání: '"Isa Ebtehaj"'
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
Autor:
Isa Ebtehaj
Publikováno v:
Frontiers in Earth Science, Vol 12 (2024)
Externí odkaz:
https://doaj.org/article/186df5607fc04ec9896f8686914405a4
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
Publikováno v:
Water Supply, Vol 22, Iss 3, Pp 2847-2862 (2022)
Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. This study includes a comparative analysis of the two machine learning techniques, M5P model tree (M5P)
Externí odkaz:
https://doaj.org/article/e82e4aa7aff2416aa1086dd9b8ad4ee8
Autor:
Jean Cardi, Antony Dussel, Clara Letessier, Isa Ebtehaj, Silvio Jose Gumiere, Hossein Bonakdari
Publikováno v:
Hydrology, Vol 10, Iss 9, p 177 (2023)
The Ottawa River Watershed is a vast area that stretches across Ontario and Quebec and holds great importance for Canada’s people, economy, and collective history, both in the present and the future. The river has faced numerous floods in recent ye
Externí odkaz:
https://doaj.org/article/eedacbf74fbe4cf0b18f9988b27bdea4
Publikováno v:
Hydrology, Vol 10, Iss 8, p 164 (2023)
Given that the primary cause of flooding in Ontario, Canada, is attributed to spring floods, it is crucial to incorporate temperature as an input variable in flood prediction models with machine learning algorithms. This inclusion enables a comprehen
Externí odkaz:
https://doaj.org/article/55fc502cd1ca4e24b9484281c2e80fb3
Publikováno v:
Agriculture, Vol 13, Iss 6, p 1163 (2023)
In the current study, a new hybrid machine learning (ML)-based model was developed by integrating a convolution neural network (CNN) with a random forest (RF) to forecast pesticide use on golf courses in Québec, Canada. Three main groups of independ
Externí odkaz:
https://doaj.org/article/6c1e7a77804242649302384c041be753
Autor:
Isa Ebtehaj, Saad Sh. Sammen, Lariyah Mohd Sidek, Anurag Malik, Parveen Sihag, Ahmed Mohammed Sami Al-Janabi, Kwok-Wing Chau, Hossein Bonakdari
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 15, Iss 1, Pp 1343-1361 (2021)
Accurate prediction of water level (WL) is essential for the optimal management of different water resource projects. The development of a reliable model for WL prediction remains a challenging task in water resources management. In this study, novel
Externí odkaz:
https://doaj.org/article/0791a01840c743deb1cf0a4813cd85dd
Publikováno v:
Environmental Sciences Proceedings, Vol 25, Iss 1, p 51 (2023)
In this study, an improved version of the outlier robust extreme learning machine (IORELM) is introduced as a new method for multi-step-ahead hourly air temperature forecasting. The proposed method was calibrated and used to estimate the hourly air t
Externí odkaz:
https://doaj.org/article/1ae7f043741a4252a3c789c44ad96c2e
Publikováno v:
Environmental Sciences Proceedings, Vol 25, Iss 1, p 50 (2023)
In this study, an improved version of the Extreme Learning Machine, namely the Improved Weighted Regularization ELM (IWRELM), is proposed for hourly precipitation forecasting that is multi-steps ahead. After finding the optimal values of the proposed
Externí odkaz:
https://doaj.org/article/cf7665a59ddb42908deeab5d6d47b400