Theo dơi
Saeid Janizadeh
Tiêu đề
Trích dẫn bởi
Trích dẫn bởi
Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh watershed, Iran
S Janizadeh, M Avand, A Jaafari, TV Phong, M Bayat, E Ahmadisharaf, ...
Sustainability 11 (19), 5426, 2019
GIS based hybrid computational approaches for flash flood susceptibility assessment
BT Pham, M Avand, S Janizadeh, TV Phong, N Al-Ansari, LS Ho, S Das, ...
Water 12 (3), 683, 2020
Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms
SS Band, S Janizadeh, S Chandra Pal, A Saha, R Chakrabortty, ...
Remote Sensing 12 (21), 3568, 2020
Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping
P Yariyan, S Janizadeh, T Van Phong, HD Nguyen, R Costache, ...
Water Resources Management 34, 3037-3053, 2020
Novel ensemble approach of deep learning neural network (DLNN) model and particle swarm optimization (PSO) algorithm for prediction of gully erosion susceptibility
SS Band, S Janizadeh, S Chandra Pal, A Saha, R Chakrabortty, M Shokri, ...
Sensors 20 (19), 5609, 2020
GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran
X Lei, W Chen, M Avand, S Janizadeh, N Kariminejad, H Shahabi, ...
Remote Sensing 12 (15), 2478, 2020
Flood susceptibility mapping using an improved analytic network process with statistical models
P Yariyan, M Avand, RA Abbaspour, A Torabi Haghighi, R Costache, ...
Geomatics, Natural Hazards and Risk 11 (1), 2282-2314, 2020
Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins
A Mosavi, M Golshan, S Janizadeh, B Choubin, AM Melesse, AA Dineva
Geocarto International 37 (9), 2541-2560, 2022
Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future
S Janizadeh, SC Pal, A Saha, I Chowdhuri, K Ahmadi, S Mirzaei, ...
Journal of Environmental Management 298, 113551, 2021
Forest fire susceptibility prediction based on machine learning models with resampling algorithms on remote sensing data
B Kalantar, N Ueda, MO Idrees, S Janizadeh, K Ahmadi, F Shabani
Remote Sensing 12 (22), 3682, 2020
A tree-based intelligence ensemble approach for spatial prediction of potential groundwater
PTTNVHN Mohammadtaghi Avanda , Saeid Janizadeha , Dieu Tien Buib,c, Viet Hoa ...
Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility
W Chen, X Lei, R Chakrabortty, SC Pal, M Sahana, S Janizadeh
Journal of Environmental Management 284, 112015, 2021
A comparative assessment of random forest and k-nearest neighbor classifiers for gully erosion susceptibility mapping
M Avand, S Janizadeh, SA Naghibi, HR Pourghasemi, ...
Water 11 (10), 2076, 2019
Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential
Y Chen, W Chen, S Chandra Pal, A Saha, I Chowdhuri, B Adeli, ...
Geocarto International 37 (19), 5564-5584, 2022
GIS-based gully erosion susceptibility mapping: a comparison of computational ensemble data mining models
VH Nhu, S Janizadeh, M Avand, W Chen, M Farzin, E Omidvar, A Shirzadi, ...
Applied Sciences 10 (6), 2039, 2020
Modelling multi-hazard threats to cultural heritage sites and environmental sustainability: The present and future scenarios
A Saha, SC Pal, M Santosh, S Janizadeh, I Chowdhuri, A Norouzi, P Roy, ...
Journal of Cleaner Production 320, 128713, 2021
Deep neural network utilizing remote sensing datasets for flood hazard susceptibility mapping in Brisbane, Australia
B Kalantar, N Ueda, V Saeidi, S Janizadeh, F Shabani, K Ahmadi, ...
Remote Sensing 13 (13), 2638, 2021
Comparative analysis of artificial intelligence models for accurate estimation of groundwater nitrate concentration
SS Band, S Janizadeh, SC Pal, I Chowdhuri, Z Siabi, A Norouzi, ...
Sensors 20 (20), 5763, 2020
Comparison of machine learning methods for mapping the stand characteristics of temperate forests using multi-spectral sentinel-2 data
K Ahmadi, B Kalantar, V Saeidi, EKG Harandi, S Janizadeh, N Ueda
Remote Sensing 12 (18), 3019, 2020
Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region
Y Chen, W Chen, S Janizadeh, GS Bhunia, A Bera, QB Pham, NTT Linh, ...
Geocarto International 37 (16), 4628-4654, 2022
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