Mohammad Saadi Mesgari
Dados Biográficos
| ID | 6312035 |
|---|---|
| NOME | Mohammad Saadi Mesgari |
| PRENOMES | Mohammad Saadi |
| SOBRENOME | Mesgari |
| ASSINATURA | MESGARI M S |
| AFILIAÇÕES | K.N.Toosi University of Technology |
| ORCID | 0000-0002-2119-5164 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 4 |
| TOTAL DE CITAÇÕES | 10 |
| OBRAS COMO AUTOR | 4 |
| OBRAS COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2018 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 2 |
A spatial agent-based modeling to allocate land use for brownfield sites on an urban scale
Cities worldwide face rapid growth, necessitating improved land use planning and redevelopment strategies. Brownfield sites—abandoned, contaminated, or underused parcels within urban areas—present challenges and opportunities for sustainable urban development. Redeveloping these lands can improve environmental and social indicators and enhance the overall quality of life. This study proposes a spatial agent-based model to support the allocation o…
A Novel Evolutionary Deep Learning Approach for PM2.5 Prediction Using Remote Sensing and Spatial–Temporal Data
Forecasting particulate matter with a diameter of 2.5 μm (PM2.5) is critical due to its significant effects on both human health and the environment. While ground-based pollution measurement stations provide highly accurate PM2.5 data, their limited number and geographic coverage present significant challenges. Recently, the use of aerosol optical depth (AOD) has emerged as a viable alternative for estimating PM2.5 levels, offering a broader spat…
Simulation of urban growth using agent-based modeling and game theory with different temporal resolutions
An Agent-Based Modeling approach for sustainable urban planning from land use and public transit perspectives
An Agent-Based Modeling approach for sustainable urban planning from land use and public transit perspectives
Simulation of urban growth using agent-based modeling and game theory with different temporal resolutions
A Novel Evolutionary Deep Learning Approach for PM2.5 Prediction Using Remote Sensing and Spatial–Temporal Data
Forecasting particulate matter with a diameter of 2.5 μm (PM2.5) is critical due to its significant effects on both human health and the environment. While ground-based pollution measurement stations provide highly accurate PM2.5 data, their limited number and geographic coverage present significant challenges. Recently, the use of aerosol optical depth (AOD) has emerged as a viable alternative for estimating PM2.5 levels, offering a broader spat…
A spatial agent-based modeling to allocate land use for brownfield sites on an urban scale
Cities worldwide face rapid growth, necessitating improved land use planning and redevelopment strategies. Brownfield sites—abandoned, contaminated, or underused parcels within urban areas—present challenges and opportunities for sustainable urban development. Redeveloping these lands can improve environmental and social indicators and enhance the overall quality of life. This study proposes a spatial agent-based model to support the allocation o…
Computer Science (3 obras) · Urban planning (3 obras) · Artificial Intelligence (2 obras) · Civil engineering (2 obras) · Economics (2 obras) · Engineering (2 obras) · Geography (2 obras) · Land use (2 obras) · Land Use and Ecosystem Services (2 obras) · Land-use planning (2 obras)