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Hossein Shafizadeh-Moghadam
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2020 – today
- 2022
- [j15]Maryam Soltanikazemi, Saeid Minaei, Hossein Shafizadeh-Moghadam
, Alireza Mahdavian:
Field-scale estimation of sugarcane leaf nitrogen content using vegetation indices and spectral bands of Sentinel-2: Application of random forest and support vector regression. Comput. Electron. Agric. 200: 107130 (2022) - [j14]Hanieh Shojaei
An efficient built-up land expansion model using a modified U-Net. Int. J. Digit. Earth 15(1): 148-163 (2022) - [j13]Reza Mirzaeitalarposhti
Digital Soil Texture Mapping and Spatial Transferability of Machine Learning Models Using Sentinel-1, Sentinel-2, and Terrain-Derived Covariates. Remote. Sens. 14(23): 5909 (2022) - [j12]Hossein Shafizadeh-Moghadam
On the spatiotemporal generalization of machine learning and ensemble models for simulating built-up land expansion. Trans. GIS 26(2): 1080-1097 (2022) - 2021
- [j11]Hossein Shafizadeh-Moghadam
Fully component selection: An efficient combination of feature selection and principal component analysis to increase model performance. Expert Syst. Appl. 186: 115678 (2021) - [j10]Hossein Shafizadeh-Moghadam
Integrating a Forward Feature Selection algorithm, Random Forest, and Cellular Automata to extrapolate urban growth in the Tehran-Karaj Region of Iran. Comput. Environ. Urban Syst. 87: 101595 (2021)
2010 – 2019
- 2019
- [j9]Hossein Shafizadeh-Moghadam
GlobeLand30 maps show four times larger gross than net land change from 2000 to 2010 in Asia. Int. J. Appl. Earth Obs. Geoinformation 78: 240-248 (2019) - [j8]Hossein Shafizadeh-Moghadam
Big data in Geohazard; pattern mining and large scale analysis of landslides in Iran. Earth Sci. Informatics 12(1): 1-17 (2019) - [j7]Hossein Shafizadeh-Moghadam
Improving spatial accuracy of urban growth simulation models using ensemble forecasting approaches. Comput. Environ. Urban Syst. 76: 91-100 (2019) - [j6]Yongjiu Feng
How much can temporally stationary factors explain cellular automata-based simulations of past and future urban growth? Comput. Environ. Urban Syst. 76: 150-162 (2019) - 2017
- [j5]Hossein Shafizadeh-Moghadam
Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. Comput. Environ. Urban Syst. 64: 297-308 (2017) - [j4]Hossein Shafizadeh-Moghadam
Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth. Comput. Environ. Urban Syst. 65: 28-40 (2017) - 2016
- [j3]Amin Tayyebi
FSAUA: A framework for sensitivity analysis and uncertainty assessment in historical and forecasted land use maps. Environ. Model. Softw. 84: 70-84 (2016) - 2015
- [j2]Hossein Shafizadeh-Moghadam
Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai. Int. J. Appl. Earth Obs. Geoinformation 35: 187-198 (2015) - [j1]Hossein Shafizadeh-Moghadam
Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study. Int. J. Geogr. Inf. Sci. 29(4): 606-623 (2015)
Coauthor Index
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