50 citations as recorded by crossref.

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20. 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
21. 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
22. Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information C. Sarker et al. 10.3390/rs11192331
23. 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
24. 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
25. 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
26. 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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28. 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
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30. Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran O. Rahmati et al. 10.3390/rs11161943
31. 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
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37. Data-Driven Modeling of Low Frequency Noise Using Capture-Emission Energy Maps J. Lee 10.3390/app11010356
38. 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
39. 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
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41. 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
42. 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
43. 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
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46. 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
47. 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
48. A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping B. Pham et al. 10.3390/w12010239
49. 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
50. 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