Show source-provided references
1. Recommendations for the quantitative analysis of landslide risk
3. A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping
10.1007/s11069-021-04844-0 ↗4. Xing, Y., Yue, J., Guo, Z., Chen, Y., Hu, J., and Travé, A. (2021). Large-scale landslide susceptibility mapping using an integrated machine learning model: A case study in the Lvliang mountains of China. Front. Earth Sci., 9.
10.3389/feart.2021.722491 ↗5. Landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal taluk, India, using weighted similar choice fuzzy model
10.1007/s11069-011-9763-2 ↗6. Mapping landslide susceptibility based on deep belief network
7. Landslide susceptibility mapping with the fusion of multi-feature SVM model based FCM sampling strategy: A case study from Shaanxi Province
10.1080/19479832.2021.1961316 ↗8. A multi temporal kernel density estimation approach for new triggered landslides forecasting and susceptibility assessment
9. A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China
10.1007/s10064-017-1010-y ↗10. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility
10.1016/j.catena.2016.11.032 ↗11. Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS
10.1016/j.catena.2016.09.007 ↗12. A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)
10.1016/j.envsoft.2016.07.005 ↗13. Arabameri, A., Saha, S., Roy, J., Chen, W., Blaschke, T., and Tien Bui, D. (2020). Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River Watershed, Iran. Remote Sens., 12.
10.3390/rs12030475 ↗14. Merghadi, A., Abderrahmane, B., and Tien Bui, D. (2018). Landslide susceptibility assessment at Mila Basin (Algeria): A comparative assessment of prediction capability of advanced machine learning methods. ISPRS Int. J. Geo-Inf., 7.
10.3390/ijgi7070268 ↗15. Jiang, Z., Wang, M., and Liu, K. (2023). Comparisons of convolutional neural network and other machine learning methods in landslide susceptibility assessment: A case study in Pingwu. Remote Sens., 15.
10.3390/rs15030798 ↗17. Wang, Z., Liu, Q., and Liu, Y. (2020). Mapping landslide susceptibility using machine learning algorithms and GIS: A case study in Shexian County, Anhui Province, China. Symmetry, 12.
10.3390/sym12121954 ↗20. Multi-kernel support vector machine considering sample optimization selection for analysis and evaluation of landslide disaster susceptibility
21. Landslide Susceptibility Evaluation Based on Combined DBSCAN Cluster Sampling and SVM Classification
24. A unified network of information considering superimposed landslide factors sequence and pixel spatial neighbourhood for landslide susceptibility mapping
28. Landslide susceptibility assessment in multiple urban slope settings with a landslide inventory augmented by InSAR techniques
10.1016/j.enggeo.2023.107342 ↗31. Ensemble learning framework for landslide susceptibility mapping: Different basic classifier and ensemble strategy
10.1016/j.gsf.2023.101645 ↗36. Exploration of severities of rainfall and runoff extremes in ungauged catchments: A case study of Lai Chi Wo in Hong Kong, China
10.1016/j.scitotenv.2018.04.024 ↗38. Common Spatial Data Infrastructure (2024, November 05). Administrative Area Data (Small Area Statistics), Available online: https://portal.csdi.gov.hk/geoportal/?from=0&size=10&cat=Administrative%20Area%20FSDT#searchPanel.
39. Geospatial Data Cloud (2024, November 05). Elevation Data. Available online: https://www.gscloud.cn/.
40. Civil Engineering and Development Department (CEDD) of Hong Kong (2024, November 05). Lithology Data, Available online: https://www.cedd.gov.hk/filemanager/eng/share/map/geo_map_2.html.
41. G.R.D.C (2024, November 05). Rainfall Data. Available online: http://www.gis5g.com/data/qxsj?id=288.
42. N.E.S.D.C (2024, November 05). NDVI Data. Available online: http://www.nesdc.org.cn/sdo/detail?id=60f68d757e28174f0e7d8d49.
45. Arabameri, A., Pradhan, B., Rezaei, K., and Lee, C.-W. (2019). Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs. Remote Sens., 11.
10.3390/rs11090999 ↗48. Turner, A.K., and Schuster, R.L. (1996). Landslides: Investigation and Mitigation, National Academy of Sciences. Transportation Research Board Special Report 247.
49. Wong, H. (2017, January 19). Forty years of slope engineering in Hong Kong. Proceedings of the HKIE Geotechnical Division Annual Seminar, Hong Kong.
52. Li, H.W.M., Lo, F.L.C., Wong, T.K.C., and Cheung, R.W.M. (2022). Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong—An Exploratory Study. Appl. Sci., 12.
10.3390/app12126017 ↗53. Influence of coal ashes on fired clay brick quality: Random forest regression and artificial neural networks modeling