60 citations as recorded by crossref.

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6. Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance N. Hoang & Q. Nguyen 10.1155/2018/6290498
7. Spatial modeling of flood probability using geo-environmental variables and machine learning models, case study: Tajan watershed, Iran M. Avand et al. 10.1016/j.asr.2021.02.011
8. Implication of novel hybrid machine learning model for flood subsidence susceptibility mapping: A representative case study in Saudi Arabia A. Al-Areeq et al. 10.1016/j.jhydrol.2024.130692
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10. A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach M. Wahba et al. 10.3390/w15234138
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13. Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data S. Surampudi & V. Kumar 10.1016/j.rsase.2024.101361
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19. Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam C. Luu et al. 10.3390/su12073058
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21. Evaluating the Performance of Multi-criteria Decision-making Techniques in Flood Susceptibility Mapping R. Mahato et al. 10.1007/s12594-023-2507-6
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23. Examination of the efficacy of machine learning approaches in the generation of flood susceptibility maps M. Wahba et al. 10.1007/s12665-024-11696-x
24. One-dimensional deep learning driven geospatial analysis for flash flood susceptibility mapping: a case study in North Central Vietnam P. Hoa et al. 10.1007/s12145-024-01285-8
25. 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. 10.1016/j.jenvman.2019.01.108
26. 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 10.3390/e24111630
27. Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information C. Sarker et al. 10.3390/rs11192331
28. Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree X. Truong et al. 10.3390/app8071046
29. Flood susceptibility mapping in Brahmaputra floodplain of Bangladesh using deep boost, deep learning neural network, and artificial neural network N. Ahmed et al. 10.1080/10106049.2021.2005698
30. Application of convolutional neural networks based on Bayesian optimization to landslide susceptibility mapping of transmission tower foundation M. Lin et al. 10.1007/s10064-023-03069-8
31. A novel voting ensemble model empowered by metaheuristic feature selection for accurate flash flood susceptibility mapping R. A. Saleh et al. 10.1080/19475705.2024.2360000
32. 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. 10.1080/17538947.2023.2252401
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34. Flood risk assessment of the population in Afghanistan: A spatial analysis of hazard, exposure, and vulnerability Q. Ikram et al. 10.1016/j.nhres.2023.09.006
35. Flood mapping based on the combination of support vector regression and Heun’s scheme J. Jang et al. 10.1016/j.jhydrol.2022.128477
36. Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran O. Rahmati et al. 10.3390/rs11161943
37. 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. 10.3390/s18113704
38. Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models S. Mahato et al. 10.1016/j.gsf.2021.101175
39. 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. 10.1016/j.envc.2024.101029
40. Integration of rotation forest and multiboost ensemble methods with forest by penalizing attributes for spatial prediction of landslide susceptible areas T. Bien et al. 10.1007/s00477-023-02521-1
41. PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches O. Rahmati et al. 10.1016/j.scitotenv.2019.02.017
42. 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. 10.1016/j.jhydrol.2020.125682
43. Integrating Harris Hawks optimization and TensorFlow deep learning for flash flood susceptibility mapping using geospatial data L. Tinh et al. 10.1007/s12145-024-01351-1
44. Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling M. Saber et al. 10.1080/19475705.2023.2203798
45. Data-Driven Modeling of Low Frequency Noise Using Capture-Emission Energy Maps J. Lee 10.3390/app11010356
46. A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran A. Arabameri et al. 10.1016/j.scitotenv.2019.01.021
47. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides D. Tien Bui et al. 10.3390/rs10101538
48. A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping V. Nhu et al. 10.3390/rs12172688
49. A novel method for asphalt pavement crack classification based on image processing and machine learning N. Hoang & Q. Nguyen 10.1007/s00366-018-0611-9
50. Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam P. Nguyen et al. 10.3390/ijerph17072473
51. Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia A. Al-Areeq et al. 10.3390/rs14215515
52. GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment B. Pham et al. 10.3390/w12030683
53. Flood susceptible prediction through the use of geospatial variables and machine learning methods N. Mahdizadeh Gharakhanlou & L. Perez 10.1016/j.jhydrol.2023.129121
54. Forecasting of flash flood susceptibility mapping using random forest regression model and geographic information systems M. Wahba et al. 10.1016/j.heliyon.2024.e33982
55. A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping D. Bui et al. 10.1016/j.catena.2019.04.009
56. 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 10.1016/j.scitotenv.2019.07.197
57. Measuring the effect of hydrological insecurity on landscape and ecological condition of floodplain wetland S. Pal et al. 10.1007/s10668-024-05248-3
58. 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. 10.1007/s41748-021-00215-8
59. Integrating remote sensing with swarm intelligence and artificial intelligence for modelling wetland habitat vulnerability in pursuance of damming R. Khatun et al. 10.1016/j.ecoinf.2021.101349
60. Building information modeling integrated with environmental flood hazard to assess the building vulnerability to flash floods M. Wahba et al. 10.1007/s00477-023-02640-9