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Landslide susceptibility assessment in Qinzhou based on rough set and semi-supervised support vector machine

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Abstract

As selection of landslide-causing indicators and training samples in landslide susceptibility assessment are the key factors in determining a model’s accuracy, the purpose of this research is to improve the accuracy of the landslide susceptibility assessment model by streamlining landslide-causing indicators and expanding training samples. To this end, rough set (RS) theory and genetic reduction method are adopted to reduce the initial 15 landslide-related indicators to 8 indicators highly correlated to landslide occurrence. Then, to tackle the problem of insufficient training samples, a semi-supervised classification method is employed to train the model classifier using labeled data and unlabeled data as mixed samples. Upon these, the landslide susceptibility assessment model of RS-SSVM is set up, with landslide susceptibility grades divided into high, medium, low, and non-prone zones in Qinzhou. Finally, the area under curve (AUC) values are used to compare and validate performances for different models. Analysis and comparison of the results denotes that the RS-SSVM method performed well as indicated by the AUC values of training datasets and verification datasets being 0.9308 and 0.9116. Meanwhile, the AUC values of RS-SSVM and SSVM are 0.9116 and 0.8522, respectively. This indicates that selecting more landslide-causing indicators does not necessarily bring better accuracy; instead, only the key impact indicators should be selected. Furthermore, overlay statistical analysis using historical landslide inventory data and assessment results shows that 71.27% of the historical landslides sites are in the high susceptibility areas accounting for 2.96% of the total area of the study area, which agrees well with the distribution features of historical landslides. Therefore, the proposed RS-SSVM method can improve the spatial cognition of the complex landslide systems aside from being applied to landslide susceptibility assessment in other places with similar regional geo-environmental conditions.

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Data Availability

All data used during the study are available in 4TU Research Data repository and can be accessed through this doi link: https://figshare.com/s/0940f9e0aeb4e6647a2e.

Code Availability

The code is not available.

References

  • AGU (2017) The Human Cost of Landslide in 2016, the Landslide Blog American Geophysical Union (AGU), http://blogs.agu.org/landslideblog/

  • Aktas H, San BT (2019) Landslide susceptibility mapping using an automatic sampling algorithm based on two level random sampling. Comput Geosci 133:104329

    Google Scholar 

  • Alvioli M, Baum RL (2016) Parallelization of the TRIGRS model for rainfall-induced landslides using the message passing interface. Environ Model Softw 81:122–135

    Google Scholar 

  • Anagnostopoulos GG, Fatichi S, Burlando P (2015) An advanced process-based distributed model for the investigation of rainfall-induced landslides: the effect of process representation and boundary conditions. Water Resour Res 51(9):7501–7523

    Google Scholar 

  • Benbouras MA (2022) Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers. Int J Sediment Res 37(5):601–618

    Google Scholar 

  • Bourenane H, Bouhadad Y, Guettouche MS, Braham M (2015) GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria). Bull Eng Geol Environ 74(2):337–355

    Google Scholar 

  • Bourenane H, Guettouche MS, Bouhadad Y, Braham M (2016) Landslide hazard mapping in the Constantine City, Northeast Algeria using frequency ratio, weighting factor, logistic regression, weights of evidence, and Analytical Hierarchy Process methods. Arab J Geosci 9(154):1–24

    Google Scholar 

  • Chen W, Fan L, Li C, Pham BT (2020) Spatial prediction of landslides using hybrid integration of artificial intelligence algorithms with frequency ratio and index of entropy in Nanzheng County. China Appl Sci 10:29

    Google Scholar 

  • Chen Y, Ming D, Xiao L, Lv X, Zhou C (2021) Landslide Susceptibility mapping using feature fusion-based CPCNN-ML in Lantau Island, Hong Kong. IEEE J-STARS 14:3625–3639

    Google Scholar 

  • Ciurleo M, Mandaglio MC, Moraci N (2019) Landslide susceptibility assessment by TRIGRS in a frequently affected shallow instability area. Landslides 16:175–188

    Google Scholar 

  • Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2013) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat Road Section in Nepal Himalaya. Nat Hazard 65(1):135–165

    Google Scholar 

  • Ding S, Zhu Z, Zhang X (2015) An overview on semi-supervised support vector machine. Neural Comput Appl 28:1–10

    Google Scholar 

  • Erener A, Mutlu A, Düzgün HS (2016) A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM). Eng Geol 203:45–55

    Google Scholar 

  • Fang Z, Wang Y, Peng L, Hong H (2021) A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geogr Inf Sci 35(2):321–347

    Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    Google Scholar 

  • Hakim WL, Rezaie F, Nur AS, Panahi M, Khosravi K, Lee CW, Lee S (2022) Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon. South Korea J Environ Manage 305:114367

    Google Scholar 

  • Heckmann T, Gegg K, Gegg A, Becht M (2014) Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Nat Hazards Earth Syst Sci 14(2):259–278

    Google Scholar 

  • Hong H, Liu J, Tien Bui D, Pradhan B, Acharya TD, Pham BT, Zhu A, Chen W, Ahma BB (2018) Landslide Susceptibility mapping using J48 decision tree with Adaboost, bagging and rotation forest ensembles in the Guangchang area (China). CATENA 163:399–413

    Google Scholar 

  • Hu T, Fan X, Wang S, Guo Z, Liu A, Huang F (2020) Landslide susceptibility evaluation of Sinan County using logistics regression model and 3S technology. B Eng Geol Environ Technol 39(2):113–121

    Google Scholar 

  • Hung LQ, Van NTH, Duc DM, Ha LTC, Son PV, Khanh NH, Binh LT (2016) Landslide susceptibility mapping by combining the Analytical Hierarchy Process and weighted linear combination methods: a case study in the Upper Lo River Catchment (Vietnam). Landslides 13(5):1285–1301

    Google Scholar 

  • Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439

    Google Scholar 

  • Kong C, Tian Y, Ma X, Weng Z, Zhang Z, Xu K (2021) Landslide susceptibility assessment based on different machine learning methods in Zhaoping County of eastern Guangxi. Remote Sens 13:3573

    Google Scholar 

  • Kumar D, Thakur M, Dubey CS, Shukla DP (2017) Landslide susceptibility mapping & prediction using support vector machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology 295:115–125

    Google Scholar 

  • Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Google Scholar 

  • Liu JP, Zeng ZP, Liu HQ, Wang HB (2011) A rough set approach to analyze factors affecting landslide incidence. Comput Geosci 37:1311–1317

    Google Scholar 

  • Lucchese LV, Oliveira G, Pedrollo OC (2021) Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using artificial neural networks. CATENA 198:105067

    Google Scholar 

  • Mallat S (2016) Understanding deep convolutional networks. Phil Trans Math Phys Eng Sci 374(2065):20150203

    Google Scholar 

  • Miao FS, Zhao FC, Wu YP, Li LW, Török Á (2023) Landslide susceptibility mapping in Three Gorges Reservoir Area based on GIS and boosting decision tree model. Stoch Env Res Risk A 37(6):2283–2303

    Google Scholar 

  • Mutlu B, Nefeslioglu HA, Sezer EA, Akcayol MA, Gokceoglu C (2019) An experimental research on the use of recurrent neural networks in landslide susceptibility mapping. ISPRS Int J Geo-Inf 8(12):578

    Google Scholar 

  • Nguyen H, Bui XN, Choi Y, Lee CW, Armaghani DJ (2021) A novel combination of whale optimization algorithm and support vector machine with different Kernel functions for prediction of blasting-induced fly-rock in quarry mines. Nat Resour Res 30:191–207

    Google Scholar 

  • Niu R, Peng L, Ye R, Wu X (2012) Landslide susceptibility assessment based on rough sets and support vector machine. J Jilin Univ 42(2):430–439

    Google Scholar 

  • Nwazelibe VE, Unigwe CO, Egbueri JC (2022) Integration and comparison of algorithmic weight of evidence and logistic regression in landslide susceptibility mapping of the Orumba North erosion-prone region. Nigeria Model Earth Syst Env 9(1):967–986

    Google Scholar 

  • Pawlak Z (1982) Rough sets. Int J Comput Infor Sci 11(5):341–356

    Google Scholar 

  • Pawlak Z, Sowinski R (1994) Rough set approach to multi-attribute decision analysis. Eur J Oper Res 72:443–459

    Google Scholar 

  • Peng L, Niu R, Bo H, Wu X, Zhao Y, Ye R (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the Three Gorges Area. China Geomorphology 1:287–301

    Google Scholar 

  • Reichenbach P, Rossi M, Malamud B, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth-Sci Rev 180:60–91

    Google Scholar 

  • Romero A, Gatta C, Camps-Valls G (2015) Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(3):1349–1362

    Google Scholar 

  • Sezer EA, Nefeslioglu HA, Osna T (2017) An expert-based landslide susceptibility mapping (LSM) module developed for netcad architect software. Comput Geosci 98:26–37

    Google Scholar 

  • Sharma S, Mahajan AK (2019) A comparative assessment of information value, frequency ratio and Analytical Hierarchy Process models for landslide susceptibility mapping of a Himalayan Watershed, India. B Eng Geol Environ 78:2431–2448

    Google Scholar 

  • Sun D, Wen H, Wang D, Xu J (2020) A random forest model of landslide susceptibility mapping based on hyper-parameter optimization using Bayes algorithm. Geomorphology 362:107201

    Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293

    Google Scholar 

  • Swiniarski RW, Skowron A (2003) Rough set methods in feature selection and recognition. Pattern Recogn Lett 24:833–849

    Google Scholar 

  • Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015) Flood susceptibility assessment using GIS-based support vector machine model with different Kernel types. CATENA 125:91–101

    Google Scholar 

  • Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, Kernel logistic regression, and logistic model tree. Landslides 13(2):361–378

    Google Scholar 

  • Wang Y, Fang Z, Wang M, Peng L, Hong H (2020) Comparative study of landslide susceptibility mapping with different recurrent neural networks. Comput Geosci 138:104445

    Google Scholar 

  • Xu K, Guo Q, Li Z, Xiao J, Qin Y, Chen D, Kong C (2015) Landslide susceptibility evaluation based on BPNN and GIS: a case of Guojiaba in the Three Gorges Reservoir Area. Int J Geogr Inf Sci 29(7):1111–1124

    Google Scholar 

  • Yang X, Liu R, Li L, Yang M, Yang Y (2020) Spatial prediction of landslide susceptibility based on the neighborhood rough set. 2020 IOP Conf Ser. Mater Sci Eng 780:072052

    Google Scholar 

  • Yu X, Gao HA (2020) Landslide susceptibility map based on spatial scale segmentation: a case study at Zigui-Badong in the Three Gorges Reservoir Area. China Plos One 15:e0229818

    Google Scholar 

  • Zhang H, Song Y, Xu S, He Y, Li Z, Yu X, Liang Y, Wu W, Wang Y (2022) Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: a case study of Wanzhou section of the Three Gorges Reservoir. China Comput Geosci 158:104966

    Google Scholar 

  • Zheng Y, Chen J, Wang C, Cheng T (2020) Application of certainty factor and random forests model in landslide susceptibility evaluation in Mangshi City. Yunnan Province B Geol Sci Technol 39(6):131–144

    Google Scholar 

  • Zhou C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemi HR (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir Area, China. Comput Geosci 112:23–37

    Google Scholar 

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Acknowledgements

The authors would like to thank the Guangxi Bureau of Land and Resources for providing the various datasets used in this paper.

Funding

This work has been supported by the National Natural Science Foundation of China (No: 41201193); Hubei Provincial Natural Science Foundation of China (No:2021CFB506); Research and Development Base for Deep Prediction and Exploration Technology of Manganese Mineral Resources [2021]4027; Science and Technology Plan Project of Guizhou Province [2020]4Y039; and Science and Technology Strategic Prospecting Project of Guizhou Province [2022] ZD003 and [2022] ZD004. The authors would like to thank the anonymous reviewers for providing valuable comments on the manuscript.

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Authors and Affiliations

  1. School of Computer, China University of Geosciences, Wuhan, 430074, China

    Chunfang Kong, Yu Li, Kun Dong, Yiping Tian & Kai Xu

  2. Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan, 430074, China

    Chunfang Kong, Yiping Tian & Kai Xu

  3. Innovation Center of Mineral Resources Exploration Engineering Technology in Bedrock Area, Ministry of Natural Resources, Guiyang, 550081, China

    Chunfang Kong, Yiping Tian & Kai Xu

Authors
  1. Chunfang Kong
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Contributions

The first author contribution statement: Chunfang Kong: Conceptualization, Methodology, Investigation, Writing—original draft, Writing—review & editing. Kun Dong: Material preparation, Methodology, Software. Yu Li: Software, Data collection and analysis. Yiping Tian: Investigation, Writing—review & editing, Supervision. Credit author: Kai Xu: Investigation, Writing—original draft, Writing—review & editing, Supervision. All authors read and approved the final manuscript.

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Correspondence to Kai Xu.

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Communicated by Qiyu Chen.

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Kong, C., Li, Y., Dong, K. et al. Landslide susceptibility assessment in Qinzhou based on rough set and semi-supervised support vector machine. Earth Sci Inform 16, 3163–3177 (2023). https://doi.org/10.1007/s12145-023-01087-4

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