Reliable assessment approach of landslide susceptibility in broad areas based on optimal slope units and negative samples involving priori knowledge

References

• Abu El-Magd, Sherif Ahmed, Sk Ajim Ali, and Quoc Bao Pham. 2021. “Spatial Modeling and Susceptibility Zonation of Landslides Using Random Forest, Naïve Bayes and K-Nearest Neighbor in a Complicated Terrain.” Earth Science Informatics 14 (3): 1227–1243. doi:10.1007/s12145-021-00653-y.
   Web of Science ®Google Scholar
• Akinci, Halil, and Mustafa Zeybek. 2021. “Comparing Classical Statistic and Machine Learning Models in Landslide Susceptibility Mapping in Ardanuc (Artvin), Turkey.” Natural Hazards 108 (2): 1515–1543. doi:10.1007/s11069-021-04743-4.
   Web of Science ®Google Scholar
• Ali, Sk Ajim, Farhana Parvin, Quoc Bao Pham, Khaled Mohamed Khedher, Mahro Dehbozorgi, Yasin Wahid Rabby, Duong Tran Anh, and Duc Hiep Nguyen. 2022. “An Ensemble Random Forest Tree with SVM, ANN, NBT, and LMT for Landslide Susceptibility Mapping in the Rangit River Watershed, India.” Natural Hazards 113 (3): 1601–1633.
   Web of Science ®Google Scholar
• Ali, Sk Ajim, Farhana Parvin, Jana Vojteková, Romulus Costache, Nguyen Thi Thuy Linh, Quoc Bao Pham, Matej Vojtek, Ljubomir Gigović, Ateeque Ahmad, and Mohammad Ali Ghorbani. 2021. “GIS-based Landslide Susceptibility Modeling: A Comparison Between Fuzzy Multi-Criteria and Machine Learning Algorithms.” Geoscience Frontiers 12 (2): 857–876. doi:10.1016/j.gsf.2020.09.004.
   Web of Science ®Google Scholar
• Carrara, A., M. Cardinali, R. Detti, F. Guzzetti, V. Pasqui, and P. Reichenbach. 1991. “GIS Techniques and Statistical Models in Evaluating Landslide Hazard.” Earth Surface Processes and Landforms 16 (5): 427–445. doi:10.1002/esp.3290160505.
   Web of Science ®Google Scholar
• Carrara, Alberto, Mauro Cardinali, Fausto Guzzetti, and Paola Reichenbach. 1995. GIS Technology in Mapping Landslide Hazard. Springer.
   Google Scholar
• Chefaoui, Rosa M., and Jorge M. Lobo. 2008. “Assessing the Effects of Pseudo-Absences on Predictive Distribution Model Performance.” Ecological Modelling 210 (4): 478–486. doi:10.1016/j.ecolmodel.2007.08.010.
   Web of Science ®Google Scholar
• Chen, Zhuo, Shouyun Liang, Yutian Ke, Zhikun Yang, and Hongliang Zhao. 2020. “Landslide Susceptibility Assessment Using Different Slope Units Based on the Evidential Belief Function Model.” Geocarto International 35 (15): 1641–1664. doi:10.1080/10106049.2019.1582716.
   Web of Science ®Google Scholar
• Costache, Romulus, Sk Ajim Ali, Farhana Parvin, Quoc Bao Pham, Alireza Arabameri, Hoang Nguyen, Anca Crăciun, and Duong Tran Anh. 2021. “Detection of Areas Prone to Flood-Induced Landslides Risk Using Certainty Factor and its Hybridization with Fahp, Xgboost and Deep Learning Neural Network.” Geocarto International p (p): 1–36. doi: 10.1080/19475705.2021.1920480.
   Google Scholar
• Dahal, RanjanKumar, Shuichi Hasegawa, Netra Prakash Bhandary, Prem Prasad Poudel, Atsuko Nonomura, and Ryuichi Yatabe. 2012. “A Replication of Landslide Hazard Mapping at Catchment Scale.” Geomatics, Natural Hazards and Risk 3 (2): 161–192. doi:10.1080/19475705.2011.629007.
   Web of Science ®Google Scholar
• Erener, A., and H. S. B. Düzgün. 2012. “Landslide Susceptibility Assessment: What are the Effects of Mapping Unit and Mapping Method?” Environmental Earth Sciences 66 (3): 859–877. doi:10.1007/s12665-011-1297-0.
   Web of Science ®Google Scholar
• Giles, Philip T., and Steven E. Franklin. 1998. “An Automated Approach to the Classification of the Slope Units Using Digital Data.” Geomorphology (Amsterdam, Netherlands) 21 (3): 251–264.
   Web of Science ®Google Scholar
• Guzzetti, Fausto, Alberto Carrara, Mauro Cardinali, and Paola Reichenbach. 1999. “Landslide Hazard Evaluation: A Review of Current Techniques and Their Application in a Multi-Scale Study, Central Italy.” Geomorphology 31 (1-4): 181–216.
   Web of Science ®Google Scholar
• Heckerman, David. 1986. “Probabilistic Interpretations for Mycin's Certainty Factors.” In Machine Intelligence and Pattern Recognition, edited by N. Laveen and John F. Kanal. Lemmer: North-Holland.
   Google Scholar
• Hu, Qiao, Yi Zhou, Shixing Wang, and Futao Wang. 2020. “Machine Learning and Fractal Theory Models for Landslide Susceptibility Mapping: Case Study from the Jinsha River Basin.” Geomorphology 351: Article 106975. doi:10.1016/j.geomorph.2019.106975.
   Web of Science ®Google Scholar
• Huang, Faming, Siyu Tao, Zhilu Chang, Jinsong Huang, Xuanmei Fan, Shui-Hua Jiang, and Wenbin Li. 2021. “Efficient and Automatic Extraction of Slope Units Based on Multi-Scale Segmentation Method for Landslide Assessments.” Landslides 18 (11): 3715–3731. doi:10.1007/s10346-021-01756-9.
   Web of Science ®Google Scholar
• Jacobs, Liesbet, Matthieu Kervyn, Paola Reichenbach, Mauro Rossi, Ivan Marchesini, Massimiliano Alvioli, and Olivier Dewitte. 2020. “Regional Susceptibility Assessments with Heterogeneous Landslide Information: Slope Unit- vs. Pixel-Based Approach.” Geomorphology 356: Article 107084.
   Web of Science ®Google Scholar
• Kavzoglu, Taskin, Emrehan Kutlug Sahin, and Ismail Colkesen. 2014. “Landslide Susceptibility Mapping Using GIS-Based Multi-Criteria Decision Analysis, Support Vector Machines, and Logistic Regression.” Landslides 11 (3): 425–439. doi:10.1007/s10346-013-0391-7.
   Web of Science ®Google Scholar
• Lin, Cheng-Han, Ming-Lang Lin, Hou-Ren Peng, and Hsi-Hung Lin. 2018. “Framework for Susceptibility Analysis of Layered Rock Slopes Considering the Dimensions of the Mapping Units and Geological Data Resolution at Various map Scales.” Engineering Geology 246: 310–325. doi:10.1016/j.enggeo.2018.10.004.
   Web of Science ®Google Scholar
• Liu, Lei-Lei, Yi-LiZhang, TingXiao and CanYang. 2022. “A Frequency Ratio–Based Sampling Strategy for Landslide Susceptibility Assessment.” Bulletin of Engineering Geology and the Environment 81(9). doi: 10.1007/s10064-022-02836-3.
   Web of Science ®Google Scholar
• Martinello, Chiara, Chiara Cappadonia, Christian Conoscenti, Valerio Agnesi, and Edoardo Rotigliano. 2021. “Optimal Slope Units Partitioning in Landslide Susceptibility Mapping.” Journal of Maps 17 (3): 152–162.
   Web of Science ®Google Scholar
• Massimiliano, Alvioli, Marchesini Ivan, Reichenbach Paola, Rossi Mauro, Ardizzone Francesca, Fiorucci Federica, and Guzzetti Fausto. 2016. “Automatic Delineation of Geomorphological Slope Units with r.Slopeunits v1.0 and Their Optimization for Landslide Susceptibility Modeling.” Geoscientific Model Development 9 (11): 3975–3991.
   Web of Science ®Google Scholar
• Pham, Quoc Bao, Yacine Achour, Sk Ajim Ali, Farhana Parvin, Matej Vojtek, Jana Vojteková, Nadhir Al-Ansari, et al. 2021. “A Comparison among Fuzzy Multi-Criteria Decision Making, Bivariate, Multivariate and Machine Learning Models in Landslide Susceptibility Mapping.” Geomatics, Natural Hazards and Risk 12 (1): 1741–1777. doi:10.1080/19475705.2021.1944330.
   Web of Science ®Google Scholar
• Philip, T. Giles, and E. Franklin Steven. 1998. “An Automated Approach to the Classification of the Slope Units Using Digital Data.” Geomorphology 21 (3): 251–264.
   Web of Science ®Google Scholar
• Qi, Junde, Bing Chen, and Dinghua Zhang. 2020. “Multi-information Fusion-Based Belt Condition Monitoring in Grinding Process Using the Improved-Mahalanobis Distance and Convolutional Neural Networks.” Journal of Manufacturing Processes 59: 302–315. doi:10.1016/j.jmapro.2020.09.061.
   Web of Science ®Google Scholar
• Romstad, B., and B. Etzelmueller. 2012. “Mean Curvature Watersheds; a Simple Method for Segmentation of a Digital Elevation Model Into Terrain Units.” Geomorphology (Amsterdam, Netherlands) 139-140: 293–302.
   Web of Science ®Google Scholar
• Romstad, B., and B. Etzelmüller. 2009. “Structuring the Digital Elevation Model Into Landform Elements Through Watershed Segmentation of Curvature.” Proceedings of Geomorphometry 31: 55.
   Google Scholar
• Shortliffe, E. H., and E. Buchanan. 1975. “A Model of Inexact Reasoning in Medicine.” Mathematical Biosciences 23 (3-4).
   Google Scholar
• Thomas, Anjana V., Sunil Saha, Jean Homian Danumah, S. Raveendran, Megha K. Prasad, R. S. Ajin, and Sekhar L. Kuriakose. 2021. “Landslide Susceptibility Zonation of Idukki District Using GIS in the Aftermath of 2018 Kerala Floods and Landslides: A Comparison of AHP and Frequency Ratio Methods.” Journal of Geovisualization and Spatial Analysis 5 (2): 21. doi:10.1007/s41651-021-00090-x.
   Google Scholar
• van Westen, C. J., T. W. J. van Asch, and R. Soeters. 2006. “Landslide Hazard and Risk Zonation—why is it Still so Difficult?” Bulletin of Engineering Geology and the Environment 65 (2): 167–184. doi:10.1007/s10064-005-0023-0.
   Web of Science ®Google Scholar
• Wang, Kai, Shaojie Zhang, Ricardo DelgadoTéllez, and Fangqiang Wei. 2019. “A new Slope Unit Extraction Method for Regional Landslide Analysis Based on Morphological Image Analysis.” Bulletin of Engineering Geology and the Environment 78 (6): 4139–4151. doi:10.1007/s10064-018-1389-0.
   Web of Science ®Google Scholar
• Xiao, Chen Chao, Yuan Tian, WenZhong Shi, QingHua Guo, and Lun Wu. 2010. “A new Method of Pseudo Absence Data Generation in Landslide Susceptibility Mapping with a Case Study of Shenzhen.” Science China Technological Sciences 53 (S1): 75–84.
   Google Scholar
• Yalcin, Ali, and Fikri Bulut. 2007. “Landslide Susceptibility Mapping Using GIS and Digital Photogrammetric Techniques: A Case Study from Ardesen (NE-Turkey).” Natural Hazards 41 (1): 201–226.
   Web of Science ®Google Scholar
• Zeng, Haowei, Qing Zhu, Yulin Ding, Han Hu, Li Chen, Xiao Xie, Min Chen, and Yanxia Yao. 2022. “Graph Neural Networks with Constraints of Environmental Consistency for Landslide Susceptibility Evaluation.” International Journal of Geographical Information Science 36 (11): 2270–2295. doi:10.1080/13658816.2022.2103819.
   Web of Science ®Google Scholar
• Zhu, A-Xing, Yamin Miao, Junzhi Liu, Shibiao Bai, Canying Zeng, Tianwu Ma, and Haoyuan Hong. 2019. “A Similarity-based Approach to Sampling Absence Data for Landslide Susceptibility Mapping Using Data-driven Methods.” Catena 183: Article 104188.
   Web of Science ®Google Scholar