| Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China Y Wang, Z Fang, H Hong Science of the total environment 666, 975-993, 2019 | 399 | 2019 |
| Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping Z Fang, Y Wang, L Peng, H Hong Computers & Geosciences 139, 104470, 2020 | 246 | 2020 |
| Flood susceptibility mapping using convolutional neural network frameworks Y Wang, Z Fang, H Hong, L Peng Journal of hydrology 582, 124482, 2020 | 245 | 2020 |
| Comparative study of landslide susceptibility mapping with different recurrent neural networks Y Wang, Z Fang, M Wang, L Peng, H Hong Computers & Geosciences 138, 104445, 2020 | 223 | 2020 |
| Predicting flood susceptibility using LSTM neural networks Z Fang, Y Wang, L Peng, H Hong Journal of Hydrology 594, 125734, 2021 | 194 | 2021 |
| A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping Z Fang, Y Wang, L Peng, H Hong International Journal of Geographical Information Science 35 (2), 321-347, 2021 | 186 | 2021 |
| Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree Y Wang, Z Fang, H Hong, R Costache, X Tang Journal of Environmental Management 289, 112449, 2021 | 106 | 2021 |
| Multiple kernel-based SVM classification of hyperspectral images by combining spectral, spatial, and semantic information Y Wang, W Yu, Z Fang Remote Sensing 12 (1), 120, 2020 | 69 | 2020 |
| Multi-hazard susceptibility mapping based on Convolutional Neural Networks K Ullah, Y Wang, Z Fang, L Wang, M Rahman Geoscience Frontiers 13 (5), 101425, 2022 | 60 | 2022 |
| Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China W Li, Z Fang, Y Wang Stochastic Environmental Research and Risk Assessment, 1-22, 2021 | 48 | 2021 |
| Landslide susceptibility mapping using rotation forest ensemble technique with different decision trees in the Three Gorges Reservoir area, China Z Fang, Y Wang, G Duan, L Peng Remote Sensing 13 (2), 238, 2021 | 42 | 2021 |
| Potential of ensemble learning to improve tree-based classifiers for landslide susceptibility mapping J Song, Y Wang, Z Fang, L Peng, H Hong IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2020 | 39 | 2020 |
| Landslide susceptibility prediction based on positive unlabeled learning coupled with adaptive sampling Z Fang, Y Wang, R Niu, L Peng IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2021 | 19 | 2021 |
| Comparison of general kernel, multiple kernel, infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction Z Fang, Y Wang, H Duan, R Niu, L Peng Stochastic Environmental Research and Risk Assessment 36 (10), 3535-3556, 2022 | 17 | 2022 |
| Space–time landslide susceptibility modeling based on data-driven methods Z Fang, Y Wang, C van Westen, L Lombardo Mathematical Geosciences 56 (6), 1335-1354, 2024 | 12 | 2024 |
| Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be Z Fang, Y Wang, C van Westen, L Lombardo International Journal of Applied Earth Observation and Geoinformation 126 …, 2024 | 10 | 2024 |
| Speech-recognition in landslide predictive modelling: A case for a next generation early warning system Z Fang, H Tanyas, T Gorum, A Dahal, Y Wang, L Lombardo Environmental Modelling & Software 170, 105833, 2023 | 10 | 2023 |
| From spatio-temporal landslide susceptibility to landslide risk forecast T Wang, A Dahal, Z Fang, C van Westen, K Yin, L Lombardo Geoscience Frontiers 15 (2), 101765, 2024 | 9 | 2024 |
| Investigating earthquake legacy effect on hillslope deformation using InSAR‐derived time series K He, L Lombardo, L Chang, N Sadhasivam, X Hu, Z Fang, A Dahal, ... Earth Surface Processes and Landforms 49 (3), 980-990, 2024 | 8 | 2024 |
| Global landslide susceptibility prediction based on the automated machine learning (AutoML) framework G Tang, Z Fang, Y Wang Geocarto International 38 (1), 2236576, 2023 | 7 | 2023 |
