Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction

References

• Abbaszadeh Shahri, A., J. Spross, F. Johansson, and S. Larsson. 2019. “Landslide Susceptibility Hazard Map in Southwest Sweden Using Artificial Neural Network.” Catena 183:104225. https://doi.org/10.1016/j.catena.2019.104225.
   Web of Science ®Google Scholar
• Ai, X., B. Sun, and X. Chen. 2022. “Construction of Small Sample Seismic Landslide Susceptibility Evaluation Model Based on Transfer Learning: A Case Study of Jiuzhaigou Earthquake.” Bulletin of Engineering Geology and the Environment 81 (3): 116. https://doi.org/10.1007/s10064-022-02601-6.
   Web of Science ®Google Scholar
• Al-Najjar, H. A. H., B. Pradhan, B. Kalantar, M. I. Sameen, M. Santosh, and A. Alamri. 2021. “Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation.” Remote Sensing 13 (16): 3281. https://doi.org/10.3390/rs13163281.
   Web of Science ®Google Scholar
• Ba, Q., Y. Chen, S. Deng, J. Yang, and H. Li. 2018. “A Comparison of Slope Units and Grid Cells as Mapping Units for Landslide Susceptibility Assessment.” Earth Science Informatics 11 (3): 373–22. https://doi.org/10.1007/s12145-018-0335-9.
   Web of Science ®Google Scholar
• Bhuyan, K., S. R. Meena, L. Nava, C. van Westen, M. Floris, and F. Catani. 2023. “Mapping Landslides Through a Temporal Lens: An Insight Toward Multi-Temporal Landslide Mapping Using the U-Net Deep Learning Model.” GIScience & Remote Sensing 60 (1): 2182057. https://doi.org/10.1080/15481603.2023.2182057.
   Web of Science ®Google Scholar
• Chang, Z., Z. Du, F. Zhang, F. Huang, J. Chen, W. Li, and Z. Guo. 2020. “Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models.” Remote Sensing 12 (3): 502. https://doi.org/10.3390/rs12030502.
   Web of Science ®Google Scholar
• Charte, D., F. Charte, S. García, M. J. Del Jesus, and F. Herrera. 2018. “A Practical Tutorial on Autoencoders for Nonlinear Feature Fusion: Taxonomy, Models, Software and Guidelines.” Information Fusion 44:78–96. https://doi.org/10.1016/j.inffus.2017.12.007.
   Web of Science ®Google Scholar
• Chen, L., Y. Ding, S. Pirasteh, H. Hu, Q. Zhu, X. Ge, H. Zeng, H. Yu, Q. Shang, and Y. Song. 2022. “Meta-Learning an Intermediate Representation for Few-Shot Prediction of Landslide Susceptibility in Large Areas.” International Journal of Applied Earth Observation and Geoinformation 110:102807. https://doi.org/10.1016/j.jag.2022.102807.
   Web of Science ®Google Scholar
• Chen, S., Z. Miao, L. Wu, and Y. He. 2020. “Application of an Incomplete Landslide Inventory and One Class Classifier to Earthquake-Induced Landslide Susceptibility Mapping.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:1649–1660. https://doi.org/10.1109/JSTARS.2020.2985088.
   Web of Science ®Google Scholar
• Chen, W., H. R. Pourghasemi, A. Kornejady, and N. Zhang. 2017. “Landslide Spatial Modeling: Introducing New Ensembles of ANN, MaxEnt, and SVM Machine Learning Techniques.” Geoderma 305:314–327. https://doi.org/10.1016/j.geoderma.2017.06.020.
   Web of Science ®Google Scholar
• Chen, J., K. Xu, Z. Zhao, X. Gan, and H. Xie. 2024. “A Cellular Automaton Integrating Spatial Case-Based Reasoning for Predicting Local Landslide Hazards.” International Journal of Geographical Information Science 38 (1): 100–127. https://doi.org/10.1080/13658816.2023.2273877.
   Web of Science ®Google Scholar
• Chowdhuri, I., S. C. Pal, A. Arabameri, P. T. T. Ngo, R. Chakrabortty, S. Malik, B. Das, and P. Roy. 2020. “Ensemble Approach to Develop Landslide Susceptibility Map in Landslide Dominated Sikkim Himalayan Region, India.” Environmental Earth Sciences 79 (20): 1–28. https://doi.org/10.1007/s12665-020-09227-5.
   Web of Science ®Google Scholar
• Chowdhuri, I., S. C. Pal, R. Chakrabortty, S. Malik, B. Das, and P. Roy. 2021. “Torrential Rainfall-Induced Landslide Susceptibility Assessment Using Machine Learning and Statistical Methods of Eastern Himalaya.” Natural Hazards 107 (1): 697–722. https://doi.org/10.1007/s11069-021-04601-3.
   Web of Science ®Google Scholar
• Chowdhuri, I., S. C. Pal, S. Janizadeh, A. Saha, K. Ahmadi, R. Chakrabortty, A. R. M. Towfiqul Islam, P. Roy, and M. Shit. 2022. “Application of Novel Deep Boosting Framework-Based Earthquake Induced Landslide Hazards Prediction Approach in Sikkim Himalaya.” Geocarto International 37 (26): 12509–12535. https://doi.org/10.1080/10106049.2022.2068675.
   Web of Science ®Google Scholar
• Conoscenti, C., E. Rotigliano, M. Cama, N. A. Caraballo-Arias, L. Lombardo, and V. Agnesi. 2016. “Exploring the Effect of Absence Selection on Landslide Susceptibility Models: A Case Study in Sicily, Italy.” Geomorphology 261:222–235. https://doi.org/10.1016/j.geomorph.2016.03.006.
   Web of Science ®Google Scholar
• Corominas, J., C. van Westen, P. Frattini, L. Cascini, J. P. Malet, S. Fotopoulou, and J. T. Smith. 2014. “Recommendations for the Quantitative Analysis of Landslide Risk.” Bulletin of Engineering Geology and the Environment 73:209–263. https://doi.org/10.1007/s10064-013-0538-8.
   Web of Science ®Google Scholar
• Cui, S., X. Pei, Y. Jiang, G. Wang, X. Fan, Q. Yang, and R. Huang. 2021. “Liquefaction within a Bedding Fault: Understanding the Initiation and Movement of the Daguangbao Landslide Triggered by the 2008 Wenchuan Earthquake (Ms = 8.0).” Engineering Geology 295:106455. https://doi.org/10.1016/j.enggeo.2021.106455.
   Web of Science ®Google Scholar
• Dai, K., J. Deng, Q. Xu, Z. Li, X. Shi, C. Hancock, N. Wen, L. Zhang, and G. Zhuo. 2022. “Interpretation and Sensitivity Analysis of the InSAR Line of Sight Displacements in Landslide Measurements.” GIScience & Remote Sensing 59 (1): 1226–1242. https://doi.org/10.1080/15481603.2022.2100054.
   Web of Science ®Google Scholar
• Dai, W., Q. Yang, G. R. Xue, and Y. Yu 2007. Boosting for Transfer Learning. In: Proceedings of the 24th international conference on Machine learning - ICML ’07. ACM Press, Corvalis, Oregon, pp. 193–200.
   Google Scholar
• Froude, M. J., and D. N. Petley. 2018. “Global Fatal Landslide Occurrence from 2004 to 2016.” Natural Hazards and Earth System Sciences 18 (8): 2161–2181. https://doi.org/10.5194/nhess-18-2161-2018.
   Web of Science ®Google Scholar
• Gariano, S. L., and F. Guzzetti. 2016. “Landslides in a Changing Climate.” Earth-Science Reviews 162:227–252. https://doi.org/10.1016/j.earscirev.2016.08.011.
   Web of Science ®Google Scholar
• He, Y., Z. A. Zhao, W. Yang, H. Yan, W. Wang, S. Yao, L. Zhang, and T. Liu. 2021. “A Unified Network of Information Considering Superimposed Landslide Factors Sequence and Pixel Spatial Neighbourhood for Landslide Susceptibility Mapping.” International Journal of Applied Earth Observation and Geoinformation 104:102508. https://doi.org/10.1016/j.jag.2021.102508.
   Web of Science ®Google Scholar
• Hinton, G. E., and R. R. Salakhutdinov. 2006. “Reducing the Dimensionality of Data with Neural Networks.” Science 313 (5786): 504–507. https://doi.org/10.1126/science.1127647.
   PubMed Web of Science ®Google Scholar
• Hong, H., Y. Miao, J. Liu, and A. X. Zhu. 2019. “Exploring the Effects of the Design and Quantity of Absence Data on the Performance of Random Forest-Based Landslide Susceptibility Mapping.” Catena 176:45–64. https://doi.org/10.1016/j.catena.2018.12.035.
   Web of Science ®Google Scholar
• Huang, F., Z. Cao, J. Guo, S. H. Jiang, S. Li, and Z. Guo. 2020a. “Comparisons of Heuristic, General Statistical and Machine Learning Models for Landslide Susceptibility Prediction and Mapping.” Catena 191:104580. https://doi.org/10.1016/j.catena.2020.104580.
   Web of Science ®Google Scholar
• Huang, F., S. Tao, D. Li, Z. Lian, F. Catani, J. Huang, K. Li, and C. Zhang. 2022a. “Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies.” Remote Sensing 14 (18): 4436. https://doi.org/10.3390/rs14184436.
   Web of Science ®Google Scholar
• Huang, F., Z. Teng, C. Yao, S. H. Jiang, F. Catani, W. Chen, and J. Huang. 2024. “Uncertainties of Landslide Susceptibility Prediction: Influences of Random Errors in Landslide Conditioning Factors and Errors Reduction by Low Pass Filter Method.” Journal of Rock Mechanics and Geotechnical Engineering 16 (1): 213–230. https://doi.org/10.1016/j.jrmge.2023.11.001.
   Google Scholar
• Huang, F., J. Yan, X. Fan, C. Yao, J. Huang, W. Chen, and H. Hong. 2022b. “Uncertainty Pattern in Landslide Susceptibility Prediction Modelling: Effects of Different Landslide Boundaries and Spatial Shape Expressions.” Geoscience Frontiers 13 (2): 101317. https://doi.org/10.1016/j.gsf.2021.101317.
   Web of Science ®Google Scholar
• Huang, F., J. Zhang, C. Zhou, Y. Wang, J. Huang, and L. Zhu. 2020b. “A Deep Learning Algorithm Using a Fully Connected Sparse Autoencoder Neural Network for Landslide Susceptibility Prediction.” Landslides 17 (1): 217–229. https://doi.org/10.1007/s10346-019-01274-9.
   Web of Science ®Google Scholar
• Jia, G., M. Alvioli, S. L. Gariano, I. Marchesini, F. Guzzetti, and Q. Tang. 2021. “A Global Landslide Non-Susceptibility Map.” Geomorphology 389:107804. https://doi.org/10.1016/j.geomorph.2021.107804.
   Web of Science ®Google Scholar
• Jiang, J., and C. Zhai 2007. Instance Weighting for Domain Adaptation in NLP. In: Proceedings of the 45th Annual Meeting of the Association Computational Linguistics, 23–30 June, Prague, Czech Republic, pp. 264–271.
   Google Scholar
• Kullback, S., and R. A. Leibler. 1965. “On Information and Sufficiency.” The Annals of Mathematical Statistics 22 (1): 79–86. https://doi.org/10.1214/aoms/1177729694.
   Google Scholar
• Lee, S., J.-H. Ryu, and I.-S. Kim. 2007. “Landslide Susceptibility Analysis and Its Verification Using Likelihood Ratio, Logistic Regression, and Artificial Neural Network Models: Case Study of Youngin, Korea.” Landslides 4 (4): 327–338. https://doi.org/10.1007/s10346-007-0088-x.
   Web of Science ®Google Scholar
• Liang, P., C. Z. Qin, and A. X. Zhu. 2021. “Comparison on Two Case-Based Reasoning Strategies of Automatically Selecting Terrain Covariates for Digital Soil Mapping.” Transactions in GIS 25 (5): 2419–2437. https://doi.org/10.1111/tgis.12831.
   Web of Science ®Google Scholar
• Lin, Q., P. Lima, S. Steger, T. Glade, T. Jiang, J. Zhang, T. Liu, and Y. Wang. 2021. “National-Scale Data-Driven Rainfall Induced Landslide Susceptibility Mapping for China by Accounting for Incomplete Landslide Data.” Geoscience Frontiers 12 (6): 101248. https://doi.org/10.1016/j.gsf.2021.101248.
   Web of Science ®Google Scholar
• Masrur, A., M. Yu, P. Mitra, D. Peuquet, and A. Taylor. 2022. “Interpretable Machine Learning for Analysing Heterogeneous Drivers of Geographic Events in Space-Time.” International Journal of Geographical Information Science 36 (4): 692–719. https://doi.org/10.1080/13658816.2021.1965608.
   Web of Science ®Google Scholar
• Moragues, S., M. G. Lenzano, M. Lanfri, S. Moreiras, E. Lannutti, and L. Lenzano. 2021. “Analytic Hierarchy Process Applied to Landslide Susceptibility Mapping of the North Branch of Argentino Lake, Argentina.” Natural Hazards 105 (1): 915–941. https://doi.org/10.1007/s11069-020-04343-8.
   Web of Science ®Google Scholar
• Nam, K., and F. Wang. 2020. “An Extreme Rainfall-Induced Landslide Susceptibility Assessment Using Autoencoder Combined with Random Forest in Shimane Prefecture, Japan.” Geoenvironmental Disasters 7 (1): 1–16. https://doi.org/10.1186/s40677-020-0143-7.
   Google Scholar
• Ouyang, S., W. Chen, H. Liu, Y. Li, and Z. Xu. 2024. “A Novel Landslide Susceptibility Prediction Framework Based on Contrastive Loss.” GIScience & Remote Sensing 61 (1): 2306740. https://doi.org/10.1080/15481603.2024.2306740.
   Web of Science ®Google Scholar
• Pal, S. C., R. Chakrabortty, A. Saha, S. K. Bozchaloei, Q. B. Pham, N. T. T. Linh, D. T. Anh, S. Janizadeh, and K. Ahmadi. 2022. “Evaluation of Debris Flow and Landslide Hazards Using Ensemble Framework of Bayesian-And Tree-Based Models.” Bulletin of Engineering Geology and the Environment 81 (1): 1–25. https://doi.org/10.1007/s10064-021-02546-2.
   Web of Science ®Google Scholar
• Pal, S. C., and I. Chowdhuri. 2019. “GIS-Based Spatial Prediction of Landslide Susceptibility Using Frequency Ratio Model of Lachung River Basin, North Sikkim, India.” SN Applied Sciences 1 (5): 1–25. https://doi.org/10.1007/s42452-019-0422-7.
   Web of Science ®Google Scholar
• Pal, S. C., B. Das, and S. Malik. 2019. “Potential Landslide Vulnerability Zonation Using Integrated Analytic Hierarchy Process and GIS Technique of Upper Rangit Catchment Area, West Sikkim, India.” Journal of the Indian Society of Remote Sensing 47 (10): 1643–1655. https://doi.org/10.1007/s12524-019-01009-2.
   Web of Science ®Google Scholar
• Panahi, M., A. Jaafari, A. Shirzadi, H. Shahabi, O. Rahmati, E. Omidvar, S. Lee, and D. T. Bui. 2021. “Deep Learning Neural Networks for Spatially Explicit Prediction of Flash Flood Probability.” Geoscience Frontiers 12 (3): 101076. https://doi.org/10.1016/j.gsf.2020.09.007.
   Web of Science ®Google Scholar
• Pan, S., and Q. Yang. 2010. “A Survey on Transfer Learning.” IEEE Transactions on Knowledge and Data Engineering 22 (10): 1345–1359. https://doi.org/10.1109/TKDE.2009.191.
   Web of Science ®Google Scholar
• Park, J. Y., S. R. Lee, D. H. Lee, Y. T. Kim, and J. S. Lee. 2019. “A Regional-Scale Landslide Early Warning Methodology Applying Statistical and Physically Based Approaches in Sequence.” Engineering Geology 260:105193. https://doi.org/10.1016/j.enggeo.2019.105193.
   Web of Science ®Google Scholar
• Pham, B. T., A. Jaafari, I. Prakash, and D. T. Bui. 2019a. “A Novel Hybrid Intelligent Model of Support Vector Machines and the MultiBoost Ensemble for Landslide Susceptibility Modeling.” Bulletin of Engineering Geology and the Environment 78 (4): 2865–2886. https://doi.org/10.1007/s10064-018-1281-y.
   Web of Science ®Google Scholar
• Pham, B. T., and I. Prakash. 2019. “Evaluation and Comparison of LogitBoost Ensemble, fisher’s Linear Discriminant Analysis, Logistic Regression and Support Vector Machines Methods for Landslide Susceptibility Mapping.” Geocarto International 34 (3): 316–333. https://doi.org/10.1080/10106049.2017.1404141.
   Web of Science ®Google Scholar
• Pham, B. T., I. Prakash, S. K. Singh, A. Shirzadi, H. Shahabi, and D. T. Bui. 2019b. “Landslide Susceptibility Modeling Using Reduced Error Pruning Trees and Different Ensemble Techniques: Hybrid Machine Learning Approaches.” Catena 175:203–218. https://doi.org/10.1016/j.catena.2018.12.018.
   Web of Science ®Google Scholar
• Qi, S., Q. Xu, H. Lan, B. Zhang, and J. Liu. 2010. “Spatial Distribution Analysis of Landslides Triggered by 2008.5.12 Wenchuan Earthquake, China.” Engineering Geology 116 (1–2): 95–108. https://doi.org/10.1016/j.enggeo.2010.07.011.
   Web of Science ®Google Scholar
• Reichenbach, P., M. Rossi, B. D. Malamud, M. Mihir, and F. Guzzetti. 2018. “A Review of Statistically-Based Landslide Susceptibility Models.” Earth-Science Reviews 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001.
   Web of Science ®Google Scholar
• Ruidas, D., R. Chakrabortty, A. R. M. T. Islam, A. Saha, and S. C. Pal. 2022. “A Novel Hybrid of Meta-Optimization Approach for Flash Flood-Susceptibility Assessment in a Monsoon-Dominated Watershed, Eastern India.” Environmental Earth Sciences 81 (5): 145. https://doi.org/10.1007/s12665-022-10269-0.
   Web of Science ®Google Scholar
• Ruidas, D., S. C. Pal, A. R. M. T. Islam, and A. Saha. 2021. “Characterization of Groundwater Potential Zones in Water-Scarce Hardrock Regions Using Data Driven Model.” Environmental Earth Sciences 80 (24): 1–18. https://doi.org/10.1007/s12665-021-10116-8.
   Web of Science ®Google Scholar
• Ruidas, D., A. Saha, A. R. M. T. Islam, R. Costache, and S. C. Pal. 2023. “Development of Geo-Environmental Factors Controlled Flash Flood Hazard Map for Emergency Relief Operation in Complex Hydro-Geomorphic Environment of Tropical River, India.” Environmental Science and Pollution Research 30 (49): 106951–106966. https://doi.org/10.1007/s11356-022-23441-7.
   PubMed Web of Science ®Google Scholar
• Seneviratne, S. I., N. Nicholls, D. Easterling, C. M. Goodess, S. Kanae, J. Kossin, and Y. Luo. 2012. “Changes in Climate Extremes and Their Impacts on the Natural Physical Environment.” In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change, edited by C. B. Field, V. Barros, T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K. J. Mach, G.-K. Plattner, S. K. Allen, M. Tignor, 109–230, Cambridge, UK: Cambridge University Press.
   Google Scholar
• Steger, S., A. Brenning, R. Bell, and T. Glade. 2017. “The Influence of Systematically Incomplete Shallow Landslide Inventories on Statistical Susceptibility Models and Suggestions for Improvements.” Landslides 14 (5): 1767–1781. https://doi.org/10.1007/s10346-017-0820-0.
   Web of Science ®Google Scholar
• Sun, D., H. Wen, D. Wang, and J. Xu. 2020. “A Random Forest Model of Landslide Susceptibility Mapping Based on Hyperparameter Optimization Using Bayes Algorithm.” Geomorphology 362:107201. https://doi.org/10.1016/j.geomorph.2020.107201.
   Web of Science ®Google Scholar
• Vakhshoori, V., and M. Zare. 2016. “Landslide Susceptibility Mapping by Comparing Weight of Evidence, Fuzzy Logic, and Frequency Ratio Methods.” Geomatics, Natural Hazards and Risk 7 (5): 1731–1752. https://doi.org/10.1080/19475705.2016.1144655.
   Web of Science ®Google Scholar
• Van Den Eeckhaut, M., J. Hervás, C. Jaedicke, J. P. Malet, L. Montanarella, and F. Nadim. 2011. “Statistical Modelling of Europe-Wide Landslide Susceptibility Using Limited Landslide Inventory Data.” Landslides 9 (3): 357–369. https://doi.org/10.1007/s10346-011-0299-z.
   Web of Science ®Google Scholar
• Wang, Y., L. Feng, S. Li, F. Ren, and Q. Du. 2020. “A Hybrid Model Considering Spatial Heterogeneity for Landslide Susceptibility Mapping in Zhejiang Province, China.” Catena 188:104425. https://doi.org/10.1016/j.catena.2019.104425.
   Web of Science ®Google Scholar
• Wang, Z., J. Goetz, and A. Brenning. 2022. “Transfer Learning for Landslide Susceptibility Modeling Using Domain Adaptation and Case-Based Reasoning.” Geoscientific Model Development 15 (23): 8765–8784. https://doi.org/10.5194/gmd-15-8765-2022.
   Web of Science ®Google Scholar
• Wang, X., S. Li, H. Liu, L. Liu, Y. Liu, S. Zeng, and Q. Tang. 2021. “Landslide Susceptibility Assessment in Wenchuan County After the 5.12 Magnitude Earthquake.” Bulletin of Engineering Geology and the Environment 80 (7): 5369–5390. https://doi.org/10.1007/s10064-021-02280-9.
   Web of Science ®Google Scholar
• Wang, C., Q. Lin, L. Wang, T. Jiang, B. Su, Y. Wang, S. K. Mondal, J. Huang, and Y. Wang. 2022. “The Influences of the Spatial Extent Selection for Non-Landslide Samples on Statistical-Based Landslide Susceptibility Modelling: A Case Study of Anhui Province in China.” Natural Hazards 112 (3): 1967–1988. https://doi.org/10.1007/s11069-022-05252-8.
   Web of Science ®Google Scholar
• Wang, H., L. Wang, and L. Zhang. 2022. “Transfer Learning Improves Landslide Susceptibility Assessment.” Gondwana Research 123:238–254. https://doi.org/10.1016/j.gr.2022.07.008.
   Web of Science ®Google Scholar
• Xu, Q., C. Ouyang, T. Jiang, X. Yuan, X. Fan, and D. Cheng. 2022. “MFFENet and ADANet: A Robust Deep Transfer Learning Method and Its Application in High Precision and Fast Cross-Scene Recognition of Earthquake-Induced Landslides.” Landslides 19 (7): 1617–1647. https://doi.org/10.1007/s10346-022-01847-1.
   Web of Science ®Google Scholar
• Yao, J., X. Yao, Y. Wang, Z. Zhao, and X. Liu. 2024. “Current Active Fault Distribution and Slip Rate Along the Middle Section of the Jiali-Chayu Fault from Sentinel-1 InSAR Observations (2017–2022).” Earth, Planets and Space 76 (1): 21. https://doi.org/10.1186/s40623-024-01962-4.
   Web of Science ®Google Scholar
• Yao, J., X. Yao, Z. Zhao, and X. Liu. 2023. “Performance Comparison of Landslide Susceptibility Mapping Under Multiple Machine-Learning Based Models Considering InSAR Deformation: A Case Study of the Upper Jinsha River.” Geomatics, Natural Hazards and Risk 14 (1): 2212833. https://doi.org/10.1080/19475705.2023.2212833.
   Web of Science ®Google Scholar
• Yates, K. L., P. J. Bouchet, M. J. Caley, K. Mengersen, C. F. Randin, S. Parnell, A. H. Fielding, et al. 2018. “Outstanding Challenges in the Transferability of Ecological Models.” Trends in Ecology & Evolution 33 (10): 790–802. https://doi.org/10.1016/j.tree.2018.08.001.
   PubMed Web of Science ®Google Scholar
• Youssef, A. M., H. R. Pourghasemi, Z. S. Pourtaghi, and M. M. Al-Katheeri. 2015. “Landslide Susceptibility Mapping Using Random Forest, Boosted Regression Tree, Classification and Regression Tree, and General Linear Models and Comparison of Their Performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia.” Landslides 13 (5): 839–856. https://doi.org/10.1007/s10346-015-0614-1.
   Web of Science ®Google Scholar
• Zeng, H., Q. Zhu, Y. Ding, H. Hu, L. Chen, X. Xie, M. Chen, and Y. Yao. 2022. “Graph Neural Networks with Constraints of Environmental Consistency for Landslide Susceptibility Evaluation.” International Journal of Geographical Information Science 36 (11): 2270–2295. https://doi.org/10.1080/13658816.2022.2103819.
   Web of Science ®Google Scholar
• Zhang, S., L. Bai, Y. Li, W. Li, and M. Xie. 2022. “Comparing Convolutional Neural Network and Machine Learning Models in Landslide Susceptibility Mapping: A Case Study in Wenchuan County.” Frontiers in Environmental Science 496. https://doi.org/10.3389/fenvs.2022.886841.
   Google Scholar
• Zhang, R., P. Isola, and A. A. Efros. 2017. “Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction.” In Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, Hawaii, USA, 1058–1067.
   Google Scholar
• Zhao, Z., and J. Chen. 2023. “A Robust Discretization Method of Factor Screening for Landslide Susceptibility Mapping Using Convolution Neural Network, Random Forest, and Logistic Regression Models.” International Journal of Digital Earth 16 (1): 408–429. https://doi.org/10.1080/17538947.2023.2174192.
   Web of Science ®Google Scholar
• Zhao, Z., J. Chen, K. Xu, H. Xie, X. Gan, and H. Xu. 2021. “A Spatial Case-Based Reasoning Method for Regional Landslide Risk Assessment.” International Journal of Applied Earth Observation and Geoinformation 102:102381. https://doi.org/10.1016/j.jag.2021.102381.
   Web of Science ®Google Scholar
• Zhao, Z., J. Chen, J. Yao, K. Xu, Y. Liao, H. Xie, and X. Gan. 2023b. “An Improved Spatial Case-Based Reasoning Considering Multiple Spatial Drivers of Geographic Events and Its Application in Landslide Susceptibility Mapping.” Catena 223:106940. https://doi.org/10.1016/j.catena.2023.106940.
   Web of Science ®Google Scholar
• Zhao, C., L. Chen, Y. Yin, X. Liu, B. Li, C. Ren, and D. Liu. 2023a. “Failure Process and Three-Dimensional Motions of Mining-Induced Jianshanying Landslide in China Observed by Optical, LiDAR and SAR Datasets.” GIScience & Remote Sensing 60 (1): 2268367. https://doi.org/10.1080/15481603.2023.2268367.
   Web of Science ®Google Scholar
• Zhu, Q., L. Chen, H. Hu, S. Pirasteh, H. Li, and X. Xie. 2020. “Unsupervised Feature Learning to Improve Transferability of Landslide Susceptibility Representations.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:3917–3930. https://doi.org/10.1109/JSTARS.2020.3006192.
   Web of Science ®Google Scholar
• Zhu, L., G. Wang, F. Huang, Y. Li, W. Chen, and H. Hong. 2022. “Landslide Susceptibility Prediction Using Sparse Feature Extraction and Machine Learning Models Based on GIS and Remote Sensing.” IEEE Geoscience and Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2021.3054029.
   Web of Science ®Google Scholar