| 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 | 328 | 2020 |
| GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve 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 | 245 | 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 | 217 | 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 | 202 | 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 | 173 | 2021 |
| 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 | 165 | 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 | 158 | 2020 |
| 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 | 151 | 2022 |
| 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 | 138 | 2020 |
| Flood susceptibility assessment by using bivariate statistics and machine learning models-a useful tool for flood risk management R Costache Water Resources Management 33 (9), 3239-3256, 2019 | 136 | 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 | 131 | 2020 |
| 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 | 130 | 2019 |
| 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 | 128 | 2020 |
| Flash-flood Potential Index mapping using weights of evidence, decision Trees models and their novel hybrid integration R Costache Stochastic Environmental Research and Risk Assessment 33 (7), 1375-1402, 2019 | 128 | 2019 |
| 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 | 122 | 2021 |
| Flash-Flood Potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models R Costache Science of the Total Environment 659, 1115-1134, 2019 | 121 | 2019 |
| 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 | 120 | 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 | 117 | 2020 |
| 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 | 111 | 2020 |
| 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 | 111 | 2017 |
Mohammadtaghi AvandPostdoc in Tarbiat Modares University在 modares.ac.ir 的电子邮件经过验证
