65 citations as recorded by crossref.

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3. A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data V. Nguyen et al. https://doi.org/10.3390/rs12091373
4. Frequency Ratio Model as Tools for Flood Susceptibility Mapping in Urbanized Areas: A Case Study from Egypt H. Megahed et al. https://doi.org/10.3390/app13169445
5. Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China H. Hong et al. https://doi.org/10.1016/j.scitotenv.2017.12.256
6. Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network: A Case Study of Vietnam H. Thi Thanh Ngo et al. https://doi.org/10.32604/cmes.2023.022566
7. Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance N. Hoang & Q. Nguyen https://doi.org/10.1155/2018/6290498
8. Spatial modeling of flood probability using geo-environmental variables and machine learning models, case study: Tajan watershed, Iran M. Avand et al. https://doi.org/10.1016/j.asr.2021.02.011
9. Implication of novel hybrid machine learning model for flood subsidence susceptibility mapping: A representative case study in Saudi Arabia A. Al-Areeq et al. https://doi.org/10.1016/j.jhydrol.2024.130692
10. An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines B. Choubin et al. https://doi.org/10.1016/j.scitotenv.2018.10.064
11. A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach M. Wahba et al. https://doi.org/10.3390/w15234138
12. Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis A. Safaei-Moghadam et al. https://doi.org/10.5194/nhess-23-1-2023
13. Spatial Prediction of Fluvial Flood in High-Frequency Tropical Cyclone Area Using TensorFlow 1D-Convolution Neural Networks and Geospatial Data N. Trong et al. https://doi.org/10.3390/rs15225429
14. Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data S. Surampudi & V. Kumar https://doi.org/10.1016/j.rsase.2024.101361
15. A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling B. Pham et al. https://doi.org/10.1007/s10064-018-1281-y
16. Transfer Learning with Attributes for Improving the Landslide Spatial Prediction Performance in Sample-Scarce Area Based on Variational Autoencoder Generative Adversarial Network M. Lin et al. https://doi.org/10.3390/land12030525
17. Novel utilization of simulated runoff as causative parameter to predict the hazard of flash floods M. Wahba et al. https://doi.org/10.1007/s12665-023-11007-w
18. Modeling Wetland Habitat Quality in the Rarh Tract of Eastern India R. Khatun & S. Das https://doi.org/10.1007/s13157-024-01849-w
19. Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm H. Shahabi et al. https://doi.org/10.1016/j.gsf.2020.10.007
20. Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam C. Luu et al. https://doi.org/10.3390/su12073058
21. A new intelligence approach based on GIS-based Multivariate Adaptive Regression Splines and metaheuristic optimization for predicting flash flood susceptible areas at high-frequency tropical typhoon area D. Tien Bui et al. https://doi.org/10.1016/j.jhydrol.2019.05.046
22. Evaluating the Performance of Multi-criteria Decision-making Techniques in Flood Susceptibility Mapping R. Mahato et al. https://doi.org/10.1007/s12594-023-2507-6
23. Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches M. Bordbar et al. https://doi.org/10.1007/s11069-021-05013-z
24. Distinguishing compound and cumulative hazards using machine learning and fuzzy logic in multi-hazard susceptibility mapping J. Einali et al. https://doi.org/10.1038/s41598-026-52012-w
25. Examination of the efficacy of machine learning approaches in the generation of flood susceptibility maps M. Wahba et al. https://doi.org/10.1007/s12665-024-11696-x
26. One-dimensional deep learning driven geospatial analysis for flash flood susceptibility mapping: a case study in North Central Vietnam P. Hoa et al. https://doi.org/10.1007/s12145-024-01285-8
27. Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam) D. Tien Bui et al. https://doi.org/10.1016/j.jenvman.2019.01.108
28. Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models N. Mahdizadeh Gharakhanlou & L. Perez https://doi.org/10.3390/e24111630
29. Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information C. Sarker et al. https://doi.org/10.3390/rs11192331
30. Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree X. Truong et al. https://doi.org/10.3390/app8071046
31. Flood susceptibility mapping in Brahmaputra floodplain of Bangladesh using deep boost, deep learning neural network, and artificial neural network N. Ahmed et al. https://doi.org/10.1080/10106049.2021.2005698
32. Application of convolutional neural networks based on Bayesian optimization to landslide susceptibility mapping of transmission tower foundation M. Lin et al. https://doi.org/10.1007/s10064-023-03069-8
33. A novel voting ensemble model empowered by metaheuristic feature selection for accurate flash flood susceptibility mapping R. A. Saleh et al. https://doi.org/10.1080/19475705.2024.2360000
34. A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area L. Melgar-García et al. https://doi.org/10.1080/17538947.2023.2252401
35. A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area D. Tien Bui et al. https://doi.org/10.1016/j.scitotenv.2019.134413
36. Flood risk assessment of the population in Afghanistan: A spatial analysis of hazard, exposure, and vulnerability Q. Ikram et al. https://doi.org/10.1016/j.nhres.2023.09.006
37. Flood mapping based on the combination of support vector regression and Heun’s scheme J. Jang et al. https://doi.org/10.1016/j.jhydrol.2022.128477
38. Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran O. Rahmati et al. https://doi.org/10.3390/rs11161943
39. A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data P. Ngo et al. https://doi.org/10.3390/s18113704
40. Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models S. Mahato et al. https://doi.org/10.1016/j.gsf.2021.101175
41. Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios A. Rifath et al. https://doi.org/10.1016/j.envc.2024.101029
42. Integration of rotation forest and multiboost ensemble methods with forest by penalizing attributes for spatial prediction of landslide susceptible areas T. Bien et al. https://doi.org/10.1007/s00477-023-02521-1
43. Artificial intelligence for remote sensing-based detection and prediction of landslides in Malaysia and Vietnam: A state-of-the-art review W. Loke et al. https://doi.org/10.1007/s11069-026-08316-1
44. PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches O. Rahmati et al. https://doi.org/10.1016/j.scitotenv.2019.02.017
45. A novel hybrid quantum-PSO and credal decision tree ensemble for tropical cyclone induced flash flood susceptibility mapping with geospatial data P. Ngo et al. https://doi.org/10.1016/j.jhydrol.2020.125682
46. Integrating Harris Hawks optimization and TensorFlow deep learning for flash flood susceptibility mapping using geospatial data L. Tinh et al. https://doi.org/10.1007/s12145-024-01351-1
47. Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling M. Saber et al. https://doi.org/10.1080/19475705.2023.2203798
48. Mapping flood susceptibility using Random Forest exploiting satellite observations and geomorphic features J. Navarro et al. https://doi.org/10.1016/j.scitotenv.2025.180592
49. Enhanced Hazard Mapping for Flood and Landslide Risks Using Memetic Programming and Machine Learning Techniques to Support Sustainable Development Goals M. Wahba et al. https://doi.org/10.1109/ACCESS.2025.3623933
50. Data-Driven Modeling of Low Frequency Noise Using Capture-Emission Energy Maps J. Lee https://doi.org/10.3390/app11010356
51. A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran A. Arabameri et al. https://doi.org/10.1016/j.scitotenv.2019.01.021
52. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides D. Tien Bui et al. https://doi.org/10.3390/rs10101538
53. A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping V. Nhu et al. https://doi.org/10.3390/rs12172688
54. A novel method for asphalt pavement crack classification based on image processing and machine learning N. Hoang & Q. Nguyen https://doi.org/10.1007/s00366-018-0611-9
55. Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam P. Nguyen et al. https://doi.org/10.3390/ijerph17072473
56. Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia A. Al-Areeq et al. https://doi.org/10.3390/rs14215515
57. GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment B. Pham et al. https://doi.org/10.3390/w12030683
58. Flood susceptible prediction through the use of geospatial variables and machine learning methods N. Mahdizadeh Gharakhanlou & L. Perez https://doi.org/10.1016/j.jhydrol.2023.129121
59. Forecasting of flash flood susceptibility mapping using random forest regression model and geographic information systems M. Wahba et al. https://doi.org/10.1016/j.heliyon.2024.e33982
60. A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping D. Bui et al. https://doi.org/10.1016/j.catena.2019.04.009
61. 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 & D. Tien Bui https://doi.org/10.1016/j.scitotenv.2019.07.197
62. Measuring the effect of hydrological insecurity on landscape and ecological condition of floodplain wetland S. Pal et al. https://doi.org/10.1007/s10668-024-05248-3
63. Multi-geospatial flood hazard modelling for a large and complex river basin with data sparsity: a case study of the Lam River Basin, Vietnam N. Dung et al. https://doi.org/10.1007/s41748-021-00215-8
64. Integrating remote sensing with swarm intelligence and artificial intelligence for modelling wetland habitat vulnerability in pursuance of damming R. Khatun et al. https://doi.org/10.1016/j.ecoinf.2021.101349
65. Building information modeling integrated with environmental flood hazard to assess the building vulnerability to flash floods M. Wahba et al. https://doi.org/10.1007/s00477-023-02640-9