Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai. - Hossein Shafizadeh-Moghadam; Marco Helbich (Int. J. Appl. Earth Obs. Geoinformation, 2015)

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study. - Hossein Shafizadeh-Moghadam; Julian Hagenauer; Manuchehr Farajzadeh; Marco Helbich (Int. J. Geogr. Inf. Sci., 2015)

FSAUA: A framework for sensitivity analysis and uncertainty assessment in historical and forecasted land use maps. - Amin Tayyebi; Amir Hossein Tayyebi; Jamal Jokar Arsanjani; Hossein Shafizadeh-Moghadam; Hichem Omrani (Environ. Model. Softw., 2016)

Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. - Hossein Shafizadeh-Moghadam; Ali Asghari; Amin Tayyebi; Mohammad Taleai (Comput. Environ. Urban Syst., 2017)

Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth. - Hossein Shafizadeh-Moghadam; Amin Tayyebi; Mohammad Ahmadlou; Mahmoud Reza Delavar; Mahdi Hasanlou (Comput. Environ. Urban Syst., 2017)

GlobeLand30 maps show four times larger gross than net land change from 2000 to 2010 in Asia. - Hossein Shafizadeh-Moghadam; Masoud Minaei; Yongjiu Feng; Robert Gilmore Pontius Jr. (Int. J. Appl. Earth Obs. Geoinformation, 2019)

Big data in Geohazard; pattern mining and large scale analysis of landslides in Iran. - Hossein Shafizadeh-Moghadam; Masoud Minaei; Himan Shahabi; Julian Hagenauer (Earth Sci. Informatics, 2019)

How much can temporally stationary factors explain cellular automata-based simulations of past and future urban growth? - Yongjiu Feng; Rong Wang; Xiaohua Tong; Hossein Shafizadeh-Moghadam (Comput. Environ. Urban Syst., 2019)

Improving spatial accuracy of urban growth simulation models using ensemble forecasting approaches. - Hossein Shafizadeh-Moghadam (Comput. Environ. Urban Syst., 2019)

Fully component selection: An efficient combination of feature selection and principal component analysis to increase model performance. - Hossein Shafizadeh-Moghadam (Expert Syst. Appl., 2021)

Integrating a Forward Feature Selection algorithm, Random Forest, and Cellular Automata to extrapolate urban growth in the Tehran-Karaj Region of Iran. - Hossein Shafizadeh-Moghadam; Masoud Minaei; Robert Gilmore Pontius Jr.; Ali Asghari; Hashem Dadashpoor (Comput. Environ. Urban Syst., 2021)

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. - Maryam Soltanikazemi; Saeid Minaei; Hossein Shafizadeh-Moghadam; Alireza Mahdavian (Comput. Electron. Agric., 2022)

An efficient built-up land expansion model using a modified U-Net. - Hanieh Shojaei; Saeed Nadi; Hossein Shafizadeh-Moghadam; Amin Tayyebi; John L. van Genderen (Int. J. Digit. Earth, 2022)

On the spatiotemporal generalization of machine learning and ensemble models for simulating built-up land expansion. - Hossein Shafizadeh-Moghadam; Roozbeh Valavi; Ali Asghari; Masoud Minaei; Yuji Murayama (Trans. GIS, 2022)

Digital Soil Texture Mapping and Spatial Transferability of Machine Learning Models Using Sentinel-1, Sentinel-2, and Terrain-Derived Covariates. - Reza Mirzaeitalarposhti; Hossein Shafizadeh-Moghadam; Ruhollah Taghizadeh-Mehrjardi; Michael Scott Demyan (Remote. Sens., 2022)

Integrating sentinel-2 and sentinel-3 for actual evapotranspiration estimation across diverse climate zones using the sen-ET plugin and machine learning models. - Shima Amani; Hossein Shafizadeh-Moghadam; Saeed Morid (Earth Sci. Informatics, 2025)