Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Majid Niazkar"'
Publikováno v:
Journal of Water and Climate Change, Vol 15, Iss 6, Pp 2595-2611 (2024)
Understanding the changes in river flow is an important prerequisite for designing hydraulic structures as well as managing surface water resources in basins. By using the LARS-WG statistical downscaling model, the outputs of the general circulation
Externí odkaz:
https://doaj.org/article/a61a69f890e3409a8c34a4b7169d18f1
Autor:
Mohammad Reza Goodarzi, Amir Reza R. Niknam, Maryam Sabaghzadeh, Mohammad Hossein Mokhtari, Majid Niazkar
Publikováno v:
Water Practice and Technology, Vol 19, Iss 6, Pp 2237-2254 (2024)
This study investigates temporal variations of two water quality indices, named turbidity and Chlorophyll-a (Chl-a), from 2016 to 2021 in the Anzali wetland. For this purpose, ground-based measurements were collected at four stations from 2016 to 201
Externí odkaz:
https://doaj.org/article/2e63109f02d64453bd3f34391c913147
Publikováno v:
Journal of Water and Climate Change, Vol 15, Iss 6, Pp iii-vi (2024)
Externí odkaz:
https://doaj.org/article/0a41efb1acbb40e1b69d2626e207d14d
Autor:
Hamid Reza Niazkar, Jalil Moshari, Abdoljavad Khajavi, Mohammad Ghorbani, Majid Niazkar, Aida Negari
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MG
Externí odkaz:
https://doaj.org/article/8450328663da4f208f321703a3b71d56
Autor:
Majid Niazkar, Reza Piraei, Andrea Menapace, Pranav Dhawan, Daniele Dalla Torre, Michele Larcher, Maurizio Righetti
Publikováno v:
Journal of Water and Climate Change, Vol 15, Iss 1, Pp 271-283 (2024)
Using the global climate model outputs without any adjustment may bring errors in water resources and climate change investigations. This study tackles the critical issue of bias correction temperature in ERA5-Land reanalysis for 10 ground stations i
Externí odkaz:
https://doaj.org/article/12f3bba05a06422296f267179b4d9ee6
Publikováno v:
Hydrology, Vol 11, Iss 10, p 163 (2024)
This paper presents a comparative analysis of machine learning (ML) models for predicting drought conditions using the Standardized Precipitation Index (SPI) for two distinct stations, one in Shiraz, Iran and one in Tridolino, Italy. Four ML models,
Externí odkaz:
https://doaj.org/article/65fa0c6309e54b358068c2bfc7f5d09e
Autor:
Hamid Reza Niazkar, Aida Negari, Hamidreza Hajirezaei, Masoumeh Ghoddusi Johari, Majid Niazkar
Publikováno v:
Middle East Journal of Cancer, Vol 15, Iss 2_Supplement (2024)
Artificial intelligence (AI) and machine learning (ML) methods have gained notable recognition for their innovative problem-solving approaches, which notably do not require understanding the problem’s physical underpinnings. AI applications in medi
Externí odkaz:
https://doaj.org/article/fd5d98ded20d4e68a5e7550e2ed0b8c5
Publikováno v:
Hydrology, Vol 11, Iss 1, p 2 (2023)
Climate change affects hydroclimatic variables, and assessing the uncertainty in future predictions is crucial. This study aims to explore variations in temperature and precipitation in the Kerman Plain under climate change impacts between 2023 and 2
Externí odkaz:
https://doaj.org/article/9b2ad7c53a3e481b95476e9824193fdf
Publikováno v:
Hydrology, Vol 9, Iss 10, p 176 (2022)
This study strives to utilize WEF resources for the sustainable development of the city, with respect to future climate change. Two diffusion scenarios of Rcp8.5 and Rcp2.6 from the 5th Assessment Report by the IPCC, with the output of the HADGEM2 mo
Externí odkaz:
https://doaj.org/article/613a78f56c6e44d7b1b06d0ded8c0fc2
Autor:
Mohammad Zakwan, Majid Niazkar
Publikováno v:
Complexity, Vol 2021 (2021)
Infiltration is a vital phenomenon in the water cycle, and consequently, estimation of infiltration rate is important for many hydrologic studies. In the present paper, different data-driven models including Multiple Linear Regression (MLR), Generali
Externí odkaz:
https://doaj.org/article/3776b48cef534ec387b1074c041a8379