|A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area|
DT Bui, ND Hoang, F Martínez-Álvarez, PTT Ngo, PV Hoa, TD Pham, ...
Science of The Total Environment 701, 134413, 2020
|GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naďve Bayes tree, bivariate statistics and logistic …|
SA Ali, F Parvin, QB Pham, M Vojtek, J Vojteková, R Costache, NTT Linh, ...
Ecological Indicators 117, 106620, 2020
|Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm|
Y Wang, H Hong, W Chen, S Li, M Panahi, K Khosravi, A Shirzadi, ...
Journal of environmental management 247, 712-729, 2019
|Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques|
R Costache, QB Pham, E Sharifi, NTT Linh, S Abba, M Vojtek, J Vojteková, ...
Remote Sensing 12 (1), 106, 2020
|GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms|
SA Ali, F Parvin, J Vojteková, R Costache, NTT Linh, QB Pham, M Vojtek, ...
Geoscience Frontiers 12 (2), 857-876, 2021
|Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms|
S Talukdar, B Ghose, Shahfahad, R Salam, S Mahato, QB Pham, ...
Stochastic Environmental Research and Risk Assessment 34, 2277-2300, 2020
|Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area|
M Mohajane, R Costache, F Karimi, QB Pham, A Essahlaoui, H Nguyen, ...
Ecological Indicators 129, 107869, 2021
|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
|Identification of areas prone toflash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning andtheir ensembles|
R Costache, D Tien Bui
Science of The Total Environment 712, 136492, 2020
|Flash-Flood Potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models|
Science of the Total Environment 659, 1115-1134, 2019
|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 assessment by using bivariate statistics and machine learning models-a useful tool for flood risk management|
Water Resources Management 33 (9), 3239-3256, 2019
|Flash-flood Potential Index mapping using weights of evidence, decision Trees models and their novel hybrid integration|
Stochastic Environmental Research and Risk Assessment 33 (7), 1375-1402, 2019
|Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania|
R Costache, DT Bui
Science of The Total Environment 691, 1098-1118, 2019
|Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models|
R Costache, H Hong, QB Pham
Science of the Total Environment 711, 134514, 2020
|Potential of hybrid data-intelligence algorithms for multi-station modelling of rainfall|
QB Pham, SI Abba, AG Usman, NTT Linh, V Gupta, A Malik, R Costache, ...
Water Resources Management 33, 5067-5087, 2019
|Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees|
R Abedi, R Costache, H Shafizadeh-Moghadam, QB Pham
Geocarto International 37 (19), 5479-5496, 2022
|Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning|
R Costache, MC Popa, DT Bui, DC Diaconu, N Ciubotaru, G Minea, ...
Journal of Hydrology 585, 124808, 2020
|Flood susceptibility assessment using novel ensemble of hyperpipes and support vector regression algorithms|
A Saha, SC Pal, A Arabameri, T Blaschke, S Panahi, I Chowdhuri, ...
Water 13 (2), 241, 2021
|Mapping flood and flooding potential indices: a methodological approach to identifying areas susceptible to flood and flooding risk. Case study: the Prahova catchment (Romania)|
L Zaharia, R Costache, R Prăvălie, G Ioana-Toroimac
Frontiers of Earth Science 11, 229-247, 2017